diff --git a/pyproject.toml b/pyproject.toml index 2c70722..58e0ecd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,9 @@ dev = [ "ruff>=0.14.10", "types-pyyaml>=6.0.12", ] +pyscf = [ + "pyscf>=2.12.1", +] [project.urls] Homepage = "https://github.com/USTC-KnowledgeComputingLab/qmp-kit" @@ -51,7 +54,7 @@ qmp = "qmp.__main__:main" qmp-kit = "qmp.__main__:main" [tool.setuptools.packages.find] -include = ["qmp"] +include = ["qmp*"] [tool.setuptools.package-data] qmp = [ diff --git a/qmp/models/pyscf.py b/qmp/models/pyscf.py new file mode 100644 index 0000000..7fe76d8 --- /dev/null +++ b/qmp/models/pyscf.py @@ -0,0 +1,403 @@ +""" +This file provides an interface to work with PySCF-style integral arrays. + +The model accepts 1-electron (h1e) and 2-electron (eri) integrals in the same +convention used by PySCF's FCI solver, converting them to the internal Hamiltonian +representation used by QMP. +""" + +import logging +import dataclasses +import numpy +import torch +import openfermion +from ..networks.mlp import WaveFunctionElectronUpDown as MlpWaveFunction +from ..networks.mlp import WaveFunctionElectron as MlpWaveFunctionElectron +from ..networks.transformers import WaveFunctionElectronUpDown as TransformersWaveFunction +from ..networks.transformers import WaveFunctionElectron as TransformersWaveFunctionElectron +from ..hamiltonian import Hamiltonian +from ..utility.model_dict import model_dict, ModelProto, NetworkProto, NetworkConfigProto + + +@dataclasses.dataclass +class ModelConfig: + """ + The configuration of the PySCF model. + + Integrals should be in chemist's notation (ij|kl), matching PySCF's convention. + """ + + # 1-electron integrals in MO basis, shape (norb, norb) + h1e: numpy.ndarray + # 2-electron repulsion integrals in MO basis, shape (norb, norb, norb, norb) + # or compressed 4-fold symmetric shape (nij, nij) where nij = norb*(norb+1)//2 + eri: numpy.ndarray + # Number of spatial orbitals + n_orbit: int + # Total number of electrons + n_electron: int + # Spin (n_alpha - n_beta, i.e. 2*S) + n_spin: int = 0 + # Constant energy offset (e.g. nuclear repulsion energy) + nuclear_repulsion: float = 0.0 + # Reference (FCI) energy for logging; defaults to zero + ref_energy: float = 0.0 + + +def _restore_eri(eri: numpy.ndarray, norb: int) -> numpy.ndarray: + """ + Restore a compressed ERI array to the full (norb, norb, norb, norb) form. + + Supports: + - 4D array of shape (norb, norb, norb, norb): returned as-is. + - 2D array of shape (nij, nij) in 4-fold symmetric format + (nij = norb*(norb+1)//2), where the lower-triangle index is + ij = i*(i+1)//2 + j for i >= j. + + Parameters + ---------- + eri : numpy.ndarray + The electron-repulsion integral array. + norb : int + Number of spatial orbitals. + + Returns + ------- + numpy.ndarray + Full (norb, norb, norb, norb) ERI array. + """ + if eri.ndim == 4: + return eri + if eri.ndim == 2: + nij = norb * (norb + 1) // 2 + if eri.shape != (nij, nij): + raise ValueError(f"For 2D ERI, expected shape ({nij}, {nij}) for norb={norb}, got {eri.shape}") + eri_full = numpy.zeros((norb, norb, norb, norb), dtype=eri.dtype) + i_idx, j_idx = numpy.tril_indices(norb) + ij_list = list(zip(i_idx.tolist(), j_idx.tolist())) + for ij, (i, j) in enumerate(ij_list): + for kl, (k, m) in enumerate(ij_list): + val = eri[ij, kl] + eri_full[i, j, k, m] = val + eri_full[j, i, k, m] = val + eri_full[i, j, m, k] = val + eri_full[j, i, m, k] = val + eri_full[k, m, i, j] = val + eri_full[m, k, i, j] = val + eri_full[k, m, j, i] = val + eri_full[m, k, j, i] = val + return eri_full + raise ValueError(f"Unsupported ERI dimensionality: {eri.ndim} (expected 2 or 4)") + + +class Model(ModelProto[ModelConfig]): + """ + This class handles models built from PySCF-style integral arrays (h1e and eri). + + The integrals are in chemist's (ij|kl) notation and are converted to the + internal spin-orbital Hamiltonian representation used by QMP. + """ + + network_dict: dict[str, type[NetworkConfigProto["Model"]]] = {} + + config_t = ModelConfig + + def __init__(self, args: ModelConfig) -> None: + n_orbit = args.n_orbit + n_electron = args.n_electron + n_spin = args.n_spin + + logging.info( + "Building PySCF model: %d orbitals, %d electrons, spin=%d", + n_orbit, + n_electron, + n_spin, + ) + + # -- 1-electron integrals ------------------------------------------------ + h1e = numpy.asarray(args.h1e, dtype=numpy.float64) + if h1e.shape != (n_orbit, n_orbit): + raise ValueError(f"Expected h1e shape ({n_orbit}, {n_orbit}), got {h1e.shape}") + energy_1: torch.Tensor = torch.as_tensor(h1e, dtype=torch.float64) + + # -- 2-electron integrals ------------------------------------------------ + eri_full = _restore_eri(numpy.asarray(args.eri, dtype=numpy.float64), n_orbit) + energy_2: torch.Tensor = torch.as_tensor(eri_full, dtype=torch.float64) + + # Apply the same permutation used in fcidump.py to convert from + # chemist's (ij|kl) notation to the form expected by OpenFermion. + energy_2 = energy_2.permute(0, 2, 3, 1).contiguous() / 2 + + # -- Build spin-orbital integrals ----------------------------------------- + energy_1_b: torch.Tensor = torch.zeros([n_orbit * 2, n_orbit * 2], dtype=torch.float64) + energy_2_b: torch.Tensor = torch.zeros( + [n_orbit * 2, n_orbit * 2, n_orbit * 2, n_orbit * 2], dtype=torch.float64 + ) + energy_1_b[0::2, 0::2] = energy_1 + energy_1_b[1::2, 1::2] = energy_1 + energy_2_b[0::2, 0::2, 0::2, 0::2] = energy_2 + energy_2_b[0::2, 1::2, 1::2, 0::2] = energy_2 + energy_2_b[1::2, 0::2, 0::2, 1::2] = energy_2 + energy_2_b[1::2, 1::2, 1::2, 1::2] = energy_2 + + # -- Convert to FermionOperator via OpenFermion --------------------------- + logging.info("Converting integrals to internal Hamiltonian representation") + interaction_operator: openfermion.InteractionOperator = openfermion.InteractionOperator( + args.nuclear_repulsion, energy_1_b.numpy(), energy_2_b.numpy() + ) # type: ignore[no-untyped-call] + fermion_operator: openfermion.FermionOperator = openfermion.get_fermion_operator(interaction_operator) # type: ignore[no-untyped-call] + openfermion_hamiltonian_dict = { + k: complex(v) + for k, v in openfermion.normal_ordered(fermion_operator).terms.items() # type: ignore[no-untyped-call] + } + self.hamiltonian = Hamiltonian(openfermion_hamiltonian_dict, kind="fermi") + logging.info("Internal Hamiltonian representation successfully created") + + self.n_qubits: int = n_orbit * 2 + self.n_electrons: int = n_electron + self.n_spins: int = n_spin + self.ref_energy: float = args.ref_energy + logging.info( + "Identified %d qubits, %d electrons, spin=%d, ref_energy=%.10f", + self.n_qubits, + self.n_electrons, + self.n_spins, + self.ref_energy, + ) + + def apply_within(self, configs_i: torch.Tensor, psi_i: torch.Tensor, configs_j: torch.Tensor) -> torch.Tensor: + return self.hamiltonian.apply_within(configs_i, psi_i, configs_j) + + def find_relative( + self, + configs_i: torch.Tensor, + psi_i: torch.Tensor, + count_selected: int, + configs_exclude: torch.Tensor | None = None, + ) -> torch.Tensor: + return self.hamiltonian.find_relative(configs_i, psi_i, count_selected, configs_exclude) + + def list_relative( + self, + configs_i: torch.Tensor, + psi_i: torch.Tensor, + configs_exclude: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + return self.hamiltonian.list_relative(configs_i, psi_i, configs_exclude) + + def diagonal_term(self, configs: torch.Tensor) -> torch.Tensor: + return self.hamiltonian.diagonal_term(configs) + + def show_config(self, config: torch.Tensor) -> str: + string = "".join(f"{i:08b}"[::-1] for i in config.cpu().numpy()) + return ( + "[" + + "".join(self._show_config_site(string[index : index + 2]) for index in range(0, self.n_qubits, 2)) + + "]" + ) + + def _show_config_site(self, string: str) -> str: + match string: + case "00": + return " " + case "10": + return "↑" + case "01": + return "↓" + case "11": + return "↕" + case _: + raise ValueError(f"Invalid string: {string}") + + +model_dict["pyscf"] = Model + + +@dataclasses.dataclass +class MlpConfig: + """ + The configuration of the MLP network. + """ + + # The hidden widths of the network + hidden: tuple[int, ...] = (512,) + + def create(self, model: Model) -> NetworkProto: + """ + Create a MLP network for the model. + """ + logging.info("Hidden layer widths: %a", self.hidden) + + network = MlpWaveFunction( + double_sites=model.n_qubits, + physical_dim=2, + is_complex=True, + spin_up=(model.n_electrons + model.n_spins) // 2, + spin_down=(model.n_electrons - model.n_spins) // 2, + hidden_size=self.hidden, + ordering=+1, + ) + + return network + + +Model.network_dict["mlp/u1u1"] = MlpConfig + + +@dataclasses.dataclass +class TransformersConfig: + """ + The configuration of the transformers network. + """ + + # Embedding dimension + embedding_dim: int = 512 + # Heads number + heads_num: int = 8 + # Feedforward dimension + feed_forward_dim: int = 2048 + # Shared expert number + shared_expert_num: int = 1 + # Routed expert number + routed_expert_num: int = 0 + # Selected expert number + selected_expert_num: int = 0 + # Network depth + depth: int = 6 + + def create(self, model: Model) -> NetworkProto: + """ + Create a transformers network for the model. + """ + logging.info( + "Transformers network configuration: " + "embedding dimension: %d, " + "number of heads: %d, " + "feed-forward dimension: %d, " + "shared expert number: %d, " + "routed expert number: %d, " + "selected expert number: %d, " + "depth: %d", + self.embedding_dim, + self.heads_num, + self.feed_forward_dim, + self.shared_expert_num, + self.routed_expert_num, + self.selected_expert_num, + self.depth, + ) + + network = TransformersWaveFunction( + double_sites=model.n_qubits, + physical_dim=2, + is_complex=True, + spin_up=(model.n_electrons + model.n_spins) // 2, + spin_down=(model.n_electrons - model.n_spins) // 2, + embedding_dim=self.embedding_dim, + heads_num=self.heads_num, + feed_forward_dim=self.feed_forward_dim, + shared_num=self.shared_expert_num, + routed_num=self.routed_expert_num, + selected_num=self.selected_expert_num, + depth=self.depth, + ordering=+1, + ) + + return network + + +Model.network_dict["transformers/u1u1"] = TransformersConfig + + +@dataclasses.dataclass +class MlpElectronConfig: + """ + The configuration of the MLP network with total electron number conservation. + """ + + # The hidden widths of the network + hidden: tuple[int, ...] = (512,) + + def create(self, model: Model) -> NetworkProto: + """ + Create a MLP network for the model. + """ + logging.info("Hidden layer widths: %a", self.hidden) + + network = MlpWaveFunctionElectron( + sites=model.n_qubits, + physical_dim=2, + is_complex=True, + electrons=model.n_electrons, + hidden_size=self.hidden, + ordering=+1, + ) + + return network + + +Model.network_dict["mlp/u1"] = MlpElectronConfig + + +@dataclasses.dataclass +class TransformersElectronConfig: + """ + The configuration of the transformers network with total electron number conservation. + """ + + # Embedding dimension + embedding_dim: int = 512 + # Heads number + heads_num: int = 8 + # Feedforward dimension + feed_forward_dim: int = 2048 + # Shared expert number + shared_expert_num: int = 1 + # Routed expert number + routed_expert_num: int = 0 + # Selected expert number + selected_expert_num: int = 0 + # Network depth + depth: int = 6 + + def create(self, model: Model) -> NetworkProto: + """ + Create a transformers network for the model. + """ + logging.info( + "Transformers network configuration: " + "embedding dimension: %d, " + "number of heads: %d, " + "feed-forward dimension: %d, " + "shared expert number: %d, " + "routed expert number: %d, " + "selected expert number: %d, " + "depth: %d", + self.embedding_dim, + self.heads_num, + self.feed_forward_dim, + self.shared_expert_num, + self.routed_expert_num, + self.selected_expert_num, + self.depth, + ) + + network = TransformersWaveFunctionElectron( + sites=model.n_qubits, + physical_dim=2, + is_complex=True, + electrons=model.n_electrons, + embedding_dim=self.embedding_dim, + heads_num=self.heads_num, + feed_forward_dim=self.feed_forward_dim, + shared_num=self.shared_expert_num, + routed_num=self.routed_expert_num, + selected_num=self.selected_expert_num, + depth=self.depth, + ordering=+1, + ) + + return network + + +Model.network_dict["transformers/u1"] = TransformersElectronConfig diff --git a/qmp/plugins/__init__.py b/qmp/plugins/__init__.py new file mode 100644 index 0000000..45157d2 --- /dev/null +++ b/qmp/plugins/__init__.py @@ -0,0 +1,3 @@ +""" +This subpackage contains plugin interfaces for third-party quantum-chemistry frameworks. +""" diff --git a/qmp/plugins/pyscf.py b/qmp/plugins/pyscf.py new file mode 100644 index 0000000..fe1726b --- /dev/null +++ b/qmp/plugins/pyscf.py @@ -0,0 +1,504 @@ +""" +PySCF-compatible FCI solver plugin for QMP. + +This module exposes a :class:`Solver` that conforms to the PySCF FCI-solver +interface:: + + solver = Solver(config) + energy, ci = solver.kernel(h1e, eri, norb, nelec) + +The ``config`` argument mirrors the YAML config accepted by ``qmp.__main__``:: + + config = { + "action": {"name": "haar", "params": {}}, + "network": {"name": "mlp/u1u1", "params": {}}, + "optimizer": {"name": "Adam", "params": {"lr": 1e-3}}, + "common": { + "device": "cpu", + "dtype": "float64", + "max_absolute_step": 10, + }, + } + +Internally the solver closely mimics :func:`qmp.__main__.main`: + +1. Imports the requested algorithm and model modules. +2. Builds a :class:`~qmp.models.pyscf.Model` from the supplied integral arrays. +3. Constructs a :class:`_PyscfRuntimeContext` that overrides + :meth:`~qmp.utility.context.RuntimeContext.folder` to use a temporary + directory and :meth:`~qmp.utility.context.RuntimeContext.create_model` to + inject the pre-built model. All other methods — including ``setup()`` and + ``save()`` — are inherited unchanged from :class:`RuntimeContext` and work + via normal polymorphism. +4. Runs the algorithm via its ``main()`` method. +5. When ``max_absolute_step`` is reached, the base ``save()`` writes the + checkpoint to the temporary directory and calls ``sys.exit(0)``. The + solver catches the resulting :class:`SystemExit` and reads the saved + checkpoint file to extract the energy and CI data. + +The returned ``ci`` dict may be passed back as ``ci0`` to warm-start +subsequent calls. +""" + +import importlib +import pathlib +import shutil +import tempfile +import typing + +import dacite +import numpy +import omegaconf +import torch + +from ..models.pyscf import Model, ModelConfig +from ..utility.action_dict import action_dict +from ..utility.context import DACITE_CAST, RuntimeContext +from ..utility.model_dict import ModelProto + + +# --------------------------------------------------------------------------- +# Internal helpers +# --------------------------------------------------------------------------- + + +class _CIArray(numpy.ndarray): + """ + A numpy array subclass that stores a QMP checkpoint dictionary. + + This allows the FCI solver to return a standard-looking CI wavefunction + (the array) while still carrying the full state (network, optimizer, etc.) + needed for a high-performance warm start in the next call to :meth:`kernel`. + """ + + def __new__(cls, input_array: numpy.ndarray, checkpoint: dict[str, typing.Any]) -> "_CIArray": + obj = numpy.asanyarray(input_array).view(cls) + obj.checkpoint = checkpoint + return obj + + def __array_finalize__(self, obj: typing.Any) -> None: + if obj is None: + return + self.checkpoint: dict[str, typing.Any] = getattr(obj, "checkpoint", {}) + + +class _PyscfRuntimeContext(RuntimeContext): + """ + A :class:`~qmp.utility.context.RuntimeContext` subclass for programmatic + (non-Hydra) use from the PySCF solver plugin. + + The only behavioural differences from the base class are: + + * :meth:`folder` returns an explicit path supplied at construction time + instead of reading the Hydra output directory. + * :meth:`create_model` returns a pre-built :class:`~qmp.models.pyscf.Model` + instead of instantiating one from the OmegaConf config. + + All other methods, including :meth:`~RuntimeContext.setup` and + :meth:`~RuntimeContext.save`, are inherited unchanged. Because they call + ``self.folder()`` internally, they automatically use the temporary + directory via normal polymorphism. + """ + + def __init__( + self, + model_instance: ModelProto, + folder_path: pathlib.Path, + **kwargs: typing.Any, + ) -> None: + super().__init__(**kwargs) + self._model_instance: ModelProto = model_instance + self._folder_path: pathlib.Path = folder_path + + def folder(self) -> pathlib.Path: + return self._folder_path + + def create_model(self, model_config: omegaconf.DictConfig) -> ModelProto: + return self._model_instance + + +def _to_pyscf_ci( + data: dict[str, typing.Any], action_name: str, norb: int, nelec: int | tuple[int, int] +) -> numpy.ndarray | None: + """ + Convert QMP sampled configurations and amplitudes to a PySCF CI vector. + """ + try: + from pyscf.fci import cistring + except ImportError: + return None + + if action_name not in ("haar", "imag"): + return None + + haar_data = data.get("haar") + if not haar_data: + return None + pool = haar_data.get("pool") + if not pool: + return None + + configs, psi = pool + if configs is None or psi is None: + return None + + if isinstance(nelec, (int, numpy.integer)): + nalpha = (int(nelec) + 1) // 2 + nbeta = int(nelec) - nalpha + else: + nalpha, nbeta = int(nelec[0]), int(nelec[1]) + + na = cistring.num_strings(norb, nalpha) + nb = cistring.num_strings(norb, nbeta) + + # Limit CI vector size to avoid memory overflow (approx 10^8 elements ~ 1.6GB) + if na * nb > 10**8: + return None + + configs_np = configs.cpu().numpy() + psi_np = psi.cpu().numpy() + + # Unpack bits: QMP order is little-endian bit-packed bytes + bits = numpy.unpackbits(configs_np, axis=1, bitorder="little") + + # Extract alpha and beta occupancy (alpha=even, beta=odd bits) + alpha_bits = bits[:, 0 : 2 * norb : 2] + beta_bits = bits[:, 1 : 2 * norb : 2] + + # Convert to integer bitmasks + pw2 = 2 ** numpy.arange(norb, dtype=numpy.uint64) + alpha_dets = (alpha_bits.astype(numpy.uint64) * pw2).sum(axis=1) + beta_dets = (beta_bits.astype(numpy.uint64) * pw2).sum(axis=1) + + # Map bitmasks to PySCF determinant indices + try: + addr_a = cistring.str2addr(norb, nalpha, alpha_dets) + addr_b = cistring.str2addr(norb, nbeta, beta_dets) + except Exception: + # Fallback for older PySCF or if vectorized call fails + addr_a = numpy.array([cistring.str2addr(norb, nalpha, int(d)) for d in alpha_dets]) + addr_b = numpy.array([cistring.str2addr(norb, nbeta, int(d)) for d in beta_dets]) + + ci_array = numpy.zeros((na, nb), dtype=psi_np.dtype) + ci_array[addr_a, addr_b] = psi_np + + return ci_array + + +def _from_pyscf_ci(ci_array: numpy.ndarray, norb: int, nelec: int | tuple[int, int]) -> dict[str, typing.Any]: + """ + Convert a PySCF CI vector back to a QMP checkpoint (sampled pool). + """ + try: + from pyscf.fci import cistring + except ImportError: + return {} + + if isinstance(nelec, (int, numpy.integer)): + nalpha = (int(nelec) + 1) // 2 + nbeta = int(nelec) - nalpha + else: + nalpha, nbeta = int(nelec[0]), int(nelec[1]) + + idx_a, idx_b = numpy.where(ci_array != 0) + amplitudes = ci_array[idx_a, idx_b] + + str_a = cistring.addrs2str(norb, nalpha, idx_a) + str_b = cistring.addrs2str(norb, nbeta, idx_b) + + n_qubytes = (2 * norb + 7) // 8 + N = len(str_a) + configs = numpy.zeros((N, n_qubytes), dtype=numpy.uint8) + + for j in range(norb): + a_bit = (str_a >> j) & 1 + b_bit = (str_b >> j) & 1 + pos_a = 2 * j + pos_b = 2 * j + 1 + configs[:, pos_a // 8] |= (a_bit << (pos_a % 8)).astype(numpy.uint8) + configs[:, pos_b // 8] |= (b_bit << (pos_b % 8)).astype(numpy.uint8) + + return { + "haar": { + "global": 0, + "local": 0, + "lanczos": 0, + "pool": (torch.from_numpy(configs), torch.from_numpy(amplitudes)), + "excited": {}, + } + } + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + + +class Solver: + """ + PySCF-compatible FCI solver backed by QMP algorithms. + + The interface mirrors that of ``pyscf.fci.direct_spin1.FCISolver``:: + + from qmp.plugins.pyscf import Solver + solver = Solver({ + "action": {"name": "haar", "params": {}}, + "network": {"name": "mlp/u1u1", "params": {}}, + "optimizer": {"name": "Adam", "params": {"lr": 1e-3}}, + "common": {"device": "cpu", "max_absolute_step": 10}, + }) + energy, ci = solver.kernel(h1e, eri, norb, nelec) + + Parameters + ---------- + config : dict or omegaconf.DictConfig + Configuration in the same format as the ``qmp.__main__`` YAML config. + Required top-level keys: ``action``, ``network``, ``optimizer``, + ``common``. The ``model`` key is ignored (the model is built + automatically from the integral arrays passed to :meth:`kernel`). + ``common.max_absolute_step`` controls how many global algorithm steps + to run before returning. + """ + + def __init__(self, config: dict[str, typing.Any] | omegaconf.DictConfig) -> None: + if isinstance(config, dict): + self.config: omegaconf.DictConfig = omegaconf.OmegaConf.create(config) + else: + self.config = config + + def kernel( + self, + h1e: numpy.ndarray, + eri: numpy.ndarray, + norb: int, + nelec: int | tuple[int, int], + ci0: dict[str, typing.Any] | numpy.ndarray | None = None, + nuclear_repulsion: float = 0.0, + ref_energy: float = 0.0, + **kwargs: typing.Any, + ) -> tuple[float, numpy.ndarray | dict[str, typing.Any]]: + """ + Solve the electronic structure problem for the given integrals. + + Parameters + ---------- + h1e : numpy.ndarray + 1-electron integrals in MO basis, shape ``(norb, norb)``, in + chemist's notation (same convention as PySCF). + eri : numpy.ndarray + 2-electron repulsion integrals in MO basis in chemist's (ij|kl) + notation. Accepted shapes: + + * ``(norb, norb, norb, norb)`` – full 4-index array. + * ``(nij, nij)`` – 4-fold symmetric compressed array + (nij = norb*(norb+1)//2). + norb : int + Number of spatial orbitals. + nelec : int or tuple[int, int] + Number of electrons. If a plain ``int``, the electrons are split + as evenly as possible between alpha and beta spin (rounding alpha + up). If a 2-tuple ``(nalpha, nbeta)``, each spin count is used + directly. + ci0 : dict or numpy.ndarray, optional + Initial guess or checkpoint data. If a ``dict``, it is treated as + the full QMP checkpoint. If a ``numpy.ndarray``, it is treated as + the PySCF CI vector and converted to a QMP pool. + nuclear_repulsion : float + Constant nuclear repulsion energy added to the Hamiltonian. + ref_energy : float + Reference energy used for logging only. + **kwargs + Ignored; present for drop-in compatibility with PySCF solvers. + + Returns + ------- + energy : float + Ground-state energy after global algorithm steps. + ci : numpy.ndarray or dict + CI wavefunction as a numpy array. The array is an instance of + ``_CIArray`` which carries the full QMP checkpoint for warm-start + compatibility. + """ + # ------------------------------------------------------------------ + # 1. Resolve electron counts + # ------------------------------------------------------------------ + if isinstance(nelec, (int, numpy.integer)): + nalpha = (int(nelec) + 1) // 2 + nbeta = int(nelec) - nalpha + else: + nalpha, nbeta = int(nelec[0]), int(nelec[1]) + + n_electron = nalpha + nbeta + n_spin = nalpha - nbeta + + # ------------------------------------------------------------------ + # 2. Dynamic imports — mirroring __main__.py + # ------------------------------------------------------------------ + importlib.import_module(f"qmp.algorithms.{self.config.action.name}") + importlib.import_module("qmp.models.pyscf") + + # ------------------------------------------------------------------ + # 3. Build PySCF model from integral arrays + # ------------------------------------------------------------------ + model = Model( + ModelConfig( + h1e=numpy.asarray(h1e, dtype=numpy.float64), + eri=numpy.asarray(eri, dtype=numpy.float64), + n_orbit=norb, + n_electron=n_electron, + n_spin=n_spin, + nuclear_repulsion=nuclear_repulsion, + ref_energy=ref_energy, + ) + ) + + # ------------------------------------------------------------------ + # 4. Build OmegaConf runtime_config — same structure as the YAML + # config, but with model.name = "pyscf" so that the algorithm + # can log the model type. create_model() is overridden so the + # params field is never read. + # ------------------------------------------------------------------ + runtime_config: omegaconf.DictConfig = omegaconf.OmegaConf.create( + { + "action": omegaconf.OmegaConf.to_container(self.config.action, resolve=True), + "model": {"name": "pyscf", "params": {}}, + "network": omegaconf.OmegaConf.to_container(self.config.network, resolve=True), + "optimizer": omegaconf.OmegaConf.to_container(self.config.optimizer, resolve=True), + } + ) + + # ------------------------------------------------------------------ + # 5. Construct _PyscfRuntimeContext from config.common + # ------------------------------------------------------------------ + common_data = omegaconf.OmegaConf.to_container(self.config.common, resolve=True) + assert isinstance(common_data, dict) + # Use dacite for type conversion (pathlib.Path, torch.device, etc.), + # matching the same approach used in __main__.py. + rt = dacite.from_dict( + data_class=RuntimeContext, + data=common_data, + config=dacite.Config(cast=DACITE_CAST), + ) + + # ------------------------------------------------------------------ + # 6. Save caller gradient state; create temp directory + # ------------------------------------------------------------------ + prev_grad_enabled = torch.is_grad_enabled() + tmpdir: pathlib.Path | None = None + try: + tmpdir = pathlib.Path(tempfile.mkdtemp()) + # Handle ci0: could be full checkpoint dict or amplitudes array + if ci0 is not None: + if isinstance(ci0, _CIArray) and ci0.checkpoint: + checkpoint_to_save = ci0.checkpoint + elif isinstance(ci0, numpy.ndarray): + checkpoint_to_save = _from_pyscf_ci(ci0, norb, (nalpha, nbeta)) + else: + checkpoint_to_save = ci0 + torch.save(checkpoint_to_save, tmpdir / "data.pth") + + context = _PyscfRuntimeContext( + model_instance=model, + folder_path=tmpdir, + parent_path=rt.parent_path, + random_seed=rt.random_seed, + checkpoint_interval=rt.checkpoint_interval, + device=rt.device, + dtype=rt.dtype, + max_absolute_step=rt.max_absolute_step, + max_relative_step=rt.max_relative_step, + ) + + # ------------------------------------------------------------------ + # 7. Setup context — loads checkpoint / ci0, configures RNG + # ------------------------------------------------------------------ + checkpoint_data = context.setup() + + # ------------------------------------------------------------------ + # 8. Instantiate algorithm — mirroring __main__.py + # ------------------------------------------------------------------ + run = dacite.from_dict( + data_class=action_dict[self.config.action.name], + data=omegaconf.OmegaConf.to_container(self.config.action.params, resolve=True), # type: ignore[arg-type] + config=dacite.Config(cast=DACITE_CAST), + ) + + # ------------------------------------------------------------------ + # 9. Run algorithm — mirroring __main__.py + # The base save() writes the checkpoint to the temp dir and + # then calls sys.exit(0) when max_absolute_step is reached. + # We catch the resulting SystemExit so kernel() can return. + # ------------------------------------------------------------------ + try: + run.main( + context=context, + runtime_config=runtime_config, + checkpoint_data=checkpoint_data, + ) + except SystemExit: + pass + + # ------------------------------------------------------------------ + # 10. Read the checkpoint that was saved by save() + # ------------------------------------------------------------------ + data_path = tmpdir / "data.pth" + if data_path.exists(): + data: dict[str, typing.Any] = torch.load(data_path, map_location="cpu", weights_only=True) + else: + data = {} + + # ------------------------------------------------------------------ + # 11. Extract energy and build ci output + # ------------------------------------------------------------------ + energy = _extract_energy(data, self.config.action.name) + # Strip the random engine state — it is stale by the next call. + full_checkpoint: dict[str, typing.Any] = {k: v for k, v in data.items() if k != "random"} + + # Try to convert to PySCF CI vector format + ci_array = _to_pyscf_ci(data, self.config.action.name, norb, (nalpha, nbeta)) + if ci_array is not None: + ci: numpy.ndarray | dict[str, typing.Any] = _CIArray(ci_array, full_checkpoint) + else: + ci = full_checkpoint + + finally: + if tmpdir is not None: + shutil.rmtree(tmpdir, ignore_errors=True) + torch.set_grad_enabled(prev_grad_enabled) + + return energy, ci + + +def _extract_energy(data: dict[str, typing.Any], action_name: str) -> float: + """ + Extract the final ground-state energy from algorithm checkpoint data. + + Parameters + ---------- + data : dict + Checkpoint data loaded from the temporary directory. + action_name : str + Name of the algorithm that produced the checkpoint. + + Returns + ------- + float + Ground-state energy, or ``0.0`` if it cannot be determined from the + checkpoint (e.g. the algorithm does not store energy in its checkpoint). + """ + if action_name in ("haar", "imag"): + haar_data = data.get("haar") + if haar_data is None: + return 0.0 + haar_global_step: int = haar_data.get("global", 0) + # In haar.py, excited states are stored with key = global_step *before* + # the increment, so the last entry key is (global_step - 1). + last_key = haar_global_step - 1 + excited: dict[int, list[typing.Any]] = haar_data.get("excited", {}) + results = excited.get(last_key) + if results: + # results is list[(energy_tensor, configs, psi)]; index 0 is ground state. + energy_val = results[0][0] + if hasattr(energy_val, "item"): + return float(energy_val.item()) + return 0.0 diff --git a/uv.lock b/uv.lock index 081301d..a3824fb 100644 --- a/uv.lock +++ b/uv.lock @@ -1,18 +1,14 @@ 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