62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum
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from cuquantum import contract
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from cuquantum import cutensornet as cutn
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from mpi4py import MPI # this line initializes MPI
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import multiprocessing
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from cupy.cuda.runtime import getDeviceCount
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def eval(qibo_circ, datatype):
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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return contract(*myconvertor.state_vector_operands())
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def eval_tn_MPI(qibo_circ, datatype):
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ncpu_threads = multiprocessing.cpu_count() // 2
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n_samples = 8
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root = 0
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comm = MPI.COMM_WORLD
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rank = comm.Get_rank()
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device_id = rank % getDeviceCount()
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cp.cuda.Device(device_id).use()
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handle = cutn.create()
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cutn.distributed_reset_configuration(handle, *cutn.get_mpi_comm_pointer(comm))
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network_opts = cutn.NetworkOptions(handle=handle, blocking="auto")
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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operands_interleave = myconvertor.state_vector_operands()
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network = cutn.Network(*operands_interleave, options=network_opts)
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network.contract_path(
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optimize={"samples": n_samples, "threads": ncpu_threads}
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) # Calculate optimal path, returns path and info
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result = network.contract()
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cutn.destroy(handle)
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if rank == root:
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return result, rank
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if __name__ == "__main__":
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from qibo.models import QFT
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import cupy as cp
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import numpy as np
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num_qubits = 10
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swaps = True
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circ_qibo = QFT(num_qubits, swaps)
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dtype = "complex128"
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sv_mpi, rank = eval_tn_MPI(circ_qibo, dtype)
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if rank == 0:
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sv_reference = eval(circ_qibo, dtype)
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state_vec = np.array(circ_qibo())
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print(f"State vector difference: {abs(sv_mpi-sv_reference).max():0.3e}")
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assert cp.allclose(sv_mpi, sv_reference)
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assert cp.allclose(sv_mpi.flatten(), state_vec)
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