jaunatisblue
4b7fc931ba
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91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
import time
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import numpy as np
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import quimb.tensor as qtn
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import cotengra as ctg
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from mpi4py import MPI
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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size = comm.Get_size()
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def build_qft(n_qubits):
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circ = qtn.Circuit(n_qubits, dtype=np.complex128)
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for i in range(n_qubits):
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circ.apply_gate('H', i)
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for j in range(i + 1, n_qubits):
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circ.apply_gate('CPHASE', np.pi / 2 ** (j - i), i, j)
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return circ
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def run_mpi(n_qubits, depth=None):
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if rank == 0:
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print(f"MPI size: {size} ranks")
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print(f"Circuit: QFT {n_qubits} qubits")
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circ = build_qft(n_qubits)
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psi = circ.psi
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# 期望值网络：<psi|Z_0|psi>
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Z = np.array([[1, 0], [0, -1]], dtype=np.complex128)
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bra = psi.conj().reindex({f'k{i}': f'b{i}' for i in range(n_qubits)})
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obs = qtn.Tensor(Z, inds=(f'k0', f'b0'))
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net = psi & obs & bra
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# 2. 所有 rank 并行搜索路径，rank 0 选全局最优
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t0 = time.perf_counter()
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repeats_per_rank = max(1, 128 // size)
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opt = ctg.HyperOptimizer(
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methods=['kahypar'],
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#methods=['greedy'],
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#max_repeats=repeats_per_rank,
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max_repeats=repeats_per_rank,
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minimize='flops',
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parallel=max(1, 96 // size),
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)
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local_tree = net.contraction_tree(optimize=opt)
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all_trees = comm.gather(local_tree, root=0)
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if rank == 0:
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tree = min(all_trees, key=lambda t: t.contraction_cost())
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t1 = time.perf_counter()
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print(f"[rank 0] Path search: {t1 - t0:.4f} s")
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else:
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tree = None
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tree = comm.bcast(tree, root=0)
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# 3. rank 0 切片，broadcast sliced_tree
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if rank == 0:
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sliced_tree = tree.slice(target_size=2**27)
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else:
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sliced_tree = None
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sliced_tree = comm.bcast(sliced_tree, root=0)
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n_slices = sliced_tree.nslices
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if rank == 0:
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print(f"Total slices: {n_slices}, each rank handles ~{n_slices // size}")
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arrays = [t.data for t in net.tensors]
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# 每个 rank 处理自己负责的切片
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t2 = time.perf_counter()
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local_result = 0.0 + 0.0j
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for i in range(rank, n_slices, size):
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local_result += sliced_tree.contract_slice(arrays, i, backend='numpy')
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t3 = time.perf_counter()
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# 4. reduce 汇总到 rank 0
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total = comm.reduce(local_result, op=MPI.SUM, root=0)
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if rank == 0:
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print(f"[rank 0] Contract: {t3 - t2:.4f} s")
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print(f"Result: {total:.10f}")
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--n_qubits", type=int, default=20)
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parser.add_argument("--depth", type=int, default=30)
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args = parser.parse_args()
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run_mpi(args.n_qubits, args.depth)
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