完善mps的vidal机制,多节点并行;补充tn搜索时dask集群搜索的方式

tools/benchmark_contract_sliced.py

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+"""MPI parallel sliced contraction using pre-sliced tree."""
+import time, pickle, os
+import numpy as np
+from mpi4py import MPI
+
+NQUBITS, NLAYERS, NCORES = 25, 10, 48
+
+comm = MPI.COMM_WORLD
+rank, size = comm.Get_rank(), comm.Get_size()
+
+os.environ['OMP_NUM_THREADS'] = str(NCORES)
+os.environ['MKL_NUM_THREADS'] = str(NCORES)
+
+import torch
+import qibo, quimb as qu
+from qibotn.observables import build_random_circuit
+
+torch.set_num_threads(NCORES)
+
+circuit = build_random_circuit(NQUBITS, NLAYERS)
+qibo.set_backend("qibotn", platform="quimb")
+backend = qibo.get_backend()
+backend.configure_tn_simulation(ansatz="tn")
+qc = backend._qibo_circuit_to_quimb(circuit, backend.circuit_ansatz)
+tn = qc.local_expectation(qu.pauli('x') & qu.pauli('z'), (0, 1), rehearse='tn')
+
+if rank == 0:
+    with open(f"data/tree_q{NQUBITS}_l{NLAYERS}_sliced.pkl", 'rb') as f:
+        tree = pickle.load(f)
+else:
+    tree = None
+tree = comm.bcast(tree, root=0)
+
+arrays = [torch.from_numpy(np.asarray(t._data)) for t in tn.tensors]
+n_slices = tree.multiplicity
+
+if rank == 0:
+    print(f"Slices: {n_slices}, Ranks: {size}, "
+          f"Peak: {tree.max_size() * 16 / 1e9:.2f} GB, "
+          f"Threads/rank: {NCORES}, Backend: torch")
+
+t0 = time.time()
+result = None
+for i in range(rank, n_slices, size):
+    val = tree.contract_slice(arrays, i, backend='torch')
+    val_np = val.cpu().numpy().reshape(-1)
+    result = val_np if result is None else result + val_np
+
+if result is None:
+    result = np.zeros(1, dtype=np.complex128)
+
+total = np.zeros_like(result) if rank == 0 else None
+comm.Reduce(result, total, root=0)
+
+if rank == 0:
+    print(f"Contract: {time.time() - t0:.4f}s  Expectation: {0.5 * total[0].real:.10f}")