533 lines
20 KiB
Python
533 lines
20 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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import multiprocessing
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from cupy.cuda.runtime import getDeviceCount
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import cupy as cp
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from qibotn.QiboCircuitToMPS import QiboCircuitToMPS
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from qibotn.mps_contraction_helper import MPSContractionHelper
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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_expectation(qibo_circ, datatype):
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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return contract(
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*myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
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)
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def eval_tn_MPI_2(qibo_circ, datatype, n_samples=8):
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"""Convert qibo circuit to tensornet (TN) format and perform contraction using multi node and multi GPU through MPI.
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The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution.
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The pathfinder looks at user defined number of samples (n_samples) iteratively to select the least costly contraction path. This is sped up with multi thread.
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After pathfinding the optimal path is used in the actual contraction to give a dense vector representation of the TN.
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"""
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from mpi4py import MPI # this line initializes MPI
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import socket
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from cuquantum import Network
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# Get the hostname
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# hostname = socket.gethostname()
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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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size = comm.Get_size()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
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device_id = rank % getDeviceCount()
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# Perform circuit conversion
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
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operands = myconvertor.state_vector_operands()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
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# Broadcast the operand data.
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# operands = comm.bcast(operands, root)
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# Assign the device for each process.
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device_id = rank % getDeviceCount()
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# dev = cp.cuda.Device(device_id)
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# free_mem, total_mem = dev.mem_info
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# print("Mem free: ",free_mem, "Total mem: ",total_mem, "rank =",rank)
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# Create network object.
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network = Network(*operands, options={"device_id": device_id})
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# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
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path, info = network.contract_path(
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optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
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)
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# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
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# Select the best path from all ranks.
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opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
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# if rank == root:
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# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
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# Broadcast info from the sender to all other ranks.
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info = comm.bcast(info, sender)
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# Set path and slices.
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path, info = network.contract_path(
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optimize={"path": info.path, "slicing": info.slices}
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)
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# Calculate this process's share of the slices.
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num_slices = info.num_slices
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chunk, extra = num_slices // size, num_slices % size
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slice_begin = rank * chunk + min(rank, extra)
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slice_end = (
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num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
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)
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slices = range(slice_begin, slice_end)
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# print(f"Process {rank} is processing slice range: {slices}.")
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# Contract the group of slices the process is responsible for.
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result = network.contract(slices=slices)
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# print(f"Process {rank} result shape is : {result.shape}.")
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# print(f"Process {rank} result size is : {result.nbytes}.")
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# Sum the partial contribution from each process on root.
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result = comm.reduce(sendobj=result, op=MPI.SUM, root=root)
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"""
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path, opt_info = network.contract_path(optimize={"samples": n_samples, "threads": ncpu_threads, 'slicing': {'min_slices': max(16, size)}})
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num_slices = opt_info.num_slices#Andy
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chunk, extra = num_slices // size, num_slices % size#Andy
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slice_begin = rank * chunk + min(rank, extra)#Andy
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slice_end = num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)#Andy
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slices = range(slice_begin, slice_end)#Andy
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result = network.contract(slices=slices)
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"""
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return result, rank
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def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
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from mpi4py import MPI # this line initializes MPI
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import socket
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from cuquantum import Network
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from cupy.cuda import nccl
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# Get the hostname
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# hostname = socket.gethostname()
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root = 0
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comm_mpi = MPI.COMM_WORLD
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rank = comm_mpi.Get_rank()
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size = comm_mpi.Get_size()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
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device_id = rank % getDeviceCount()
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cp.cuda.Device(device_id).use()
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# Set up the NCCL communicator.
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nccl_id = nccl.get_unique_id() if rank == root else None
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nccl_id = comm_mpi.bcast(nccl_id, root)
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comm_nccl = nccl.NcclCommunicator(size, nccl_id, rank)
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# Perform circuit conversion
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
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operands = myconvertor.state_vector_operands()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
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network = Network(*operands)
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# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
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path, info = network.contract_path(
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optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
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)
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# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
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# Select the best path from all ranks.
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opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
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# if rank == root:
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# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
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# Broadcast info from the sender to all other ranks.
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info = comm_mpi.bcast(info, sender)
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# Set path and slices.
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path, info = network.contract_path(
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optimize={"path": info.path, "slicing": info.slices}
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)
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# Calculate this process's share of the slices.
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num_slices = info.num_slices
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chunk, extra = num_slices // size, num_slices % size
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slice_begin = rank * chunk + min(rank, extra)
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slice_end = (
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num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
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)
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slices = range(slice_begin, slice_end)
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# print(f"Process {rank} is processing slice range: {slices}.")
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# Contract the group of slices the process is responsible for.
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result = network.contract(slices=slices)
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# print(f"Process {rank} result shape is : {result.shape}.")
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# print(f"Process {rank} result size is : {result.nbytes}.")
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# Sum the partial contribution from each process on root.
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stream_ptr = cp.cuda.get_current_stream().ptr
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comm_nccl.reduce(
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result.data.ptr,
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result.data.ptr,
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result.size,
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nccl.NCCL_FLOAT64,
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nccl.NCCL_SUM,
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root,
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stream_ptr,
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)
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return result, rank
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def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
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from mpi4py import MPI # this line initializes MPI
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import socket
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from cuquantum import Network
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from cupy.cuda import nccl
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# Get the hostname
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# hostname = socket.gethostname()
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root = 0
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comm_mpi = MPI.COMM_WORLD
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rank = comm_mpi.Get_rank()
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size = comm_mpi.Get_size()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
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device_id = rank % getDeviceCount()
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cp.cuda.Device(device_id).use()
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# Set up the NCCL communicator.
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nccl_id = nccl.get_unique_id() if rank == root else None
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nccl_id = comm_mpi.bcast(nccl_id, root)
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comm_nccl = nccl.NcclCommunicator(size, nccl_id, rank)
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# Perform circuit conversion
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
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operands = myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
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network = Network(*operands)
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# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
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path, info = network.contract_path(
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optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
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)
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# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
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# Select the best path from all ranks.
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opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
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# if rank == root:
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# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
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# Broadcast info from the sender to all other ranks.
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info = comm_mpi.bcast(info, sender)
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# Set path and slices.
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path, info = network.contract_path(
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optimize={"path": info.path, "slicing": info.slices}
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)
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# Calculate this process's share of the slices.
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num_slices = info.num_slices
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chunk, extra = num_slices // size, num_slices % size
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slice_begin = rank * chunk + min(rank, extra)
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slice_end = (
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num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
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)
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slices = range(slice_begin, slice_end)
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# print(f"Process {rank} is processing slice range: {slices}.")
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# Contract the group of slices the process is responsible for.
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result = network.contract(slices=slices)
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# print(f"Process {rank} result shape is : {result.shape}.")
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# print(f"Process {rank} result size is : {result.nbytes}.")
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# Sum the partial contribution from each process on root.
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stream_ptr = cp.cuda.get_current_stream().ptr
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comm_nccl.reduce(
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result.data.ptr,
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result.data.ptr,
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result.size,
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nccl.NCCL_FLOAT64,
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nccl.NCCL_SUM,
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root,
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stream_ptr,
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)
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return result, rank
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def eval_tn_MPI_2_expectation(qibo_circ, datatype, n_samples=8):
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from mpi4py import MPI # this line initializes MPI
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import socket
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from cuquantum import Network
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# Get the hostname
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# hostname = socket.gethostname()
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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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size = comm.Get_size()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
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device_id = rank % getDeviceCount()
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# Perform circuit conversion
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
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operands = myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
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# Broadcast the operand data.
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# operands = comm.bcast(operands, root)
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# Assign the device for each process.
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device_id = rank % getDeviceCount()
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# dev = cp.cuda.Device(device_id)
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# free_mem, total_mem = dev.mem_info
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# print("Mem free: ",free_mem, "Total mem: ",total_mem, "rank =",rank)
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# Create network object.
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network = Network(*operands, options={"device_id": device_id})
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# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
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path, info = network.contract_path(
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optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
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)
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# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
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# Select the best path from all ranks.
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opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
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# if rank == root:
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# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
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# Broadcast info from the sender to all other ranks.
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info = comm.bcast(info, sender)
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# Set path and slices.
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path, info = network.contract_path(
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optimize={"path": info.path, "slicing": info.slices}
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)
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# Calculate this process's share of the slices.
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num_slices = info.num_slices
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chunk, extra = num_slices // size, num_slices % size
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slice_begin = rank * chunk + min(rank, extra)
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slice_end = (
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num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
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)
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slices = range(slice_begin, slice_end)
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# print(f"Process {rank} is processing slice range: {slices}.")
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# Contract the group of slices the process is responsible for.
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result = network.contract(slices=slices)
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# print(f"Process {rank} result shape is : {result.shape}.")
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# print(f"Process {rank} result size is : {result.nbytes}.")
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# Sum the partial contribution from each process on root.
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result = comm.reduce(sendobj=result, op=MPI.SUM, root=root)
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return result, rank
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def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
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from mpi4py import MPI # this line initializes MPI
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import socket
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# Get the hostname
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# hostname = socket.gethostname()
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ncpu_threads = multiprocessing.cpu_count() // 2
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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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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
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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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network_opts = cutn.NetworkOptions(handle=handle, blocking="auto")
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft network opts",mem_avail, "rank =",rank)
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cutn.distributed_reset_configuration(handle, *cutn.get_mpi_comm_pointer(comm))
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft distributed reset config",mem_avail, "rank =",rank)
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# Perform circuit conversion
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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operands_interleave = myconvertor.expectation_operands(
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PauliStringGen(qibo_circ.nqubits)
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)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
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# Pathfinder: To search for the optimal path. Optimal path are assigned to path and info attribute of the network object.
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network = cutn.Network(*operands_interleave, options=network_opts)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft cutn.Network(*operands_interleave,",mem_avail, "rank =",rank)
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path, opt_info = network.contract_path(
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optimize={
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"samples": n_samples,
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"threads": ncpu_threads,
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"slicing": {"min_slices": max(16, size)},
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}
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)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft contract path",mem_avail, "rank =",rank)
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# Execution: To execute the contraction using the optimal path found previously
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# print("opt_cost",opt_info.opt_cost, "Process =",rank)
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num_slices = opt_info.num_slices # Andy
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chunk, extra = num_slices // size, num_slices % size # Andy
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slice_begin = rank * chunk + min(rank, extra) # Andy
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slice_end = (
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num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
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) # Andy
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slices = range(slice_begin, slice_end) # Andy
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result = network.contract(slices=slices)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft contract",mem_avail, "rank =",rank)
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cutn.destroy(handle)
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return result, rank
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def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
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"""Convert qibo circuit to tensornet (TN) format and perform contraction using multi node and multi GPU through MPI.
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The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution.
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The pathfinder looks at user defined number of samples (n_samples) iteratively to select the least costly contraction path. This is sped up with multi thread.
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After pathfinding the optimal path is used in the actual contraction to give a dense vector representation of the TN.
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"""
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from mpi4py import MPI # this line initializes MPI
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import socket
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# Get the hostname
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# hostname = socket.gethostname()
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ncpu_threads = multiprocessing.cpu_count() // 2
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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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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
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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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network_opts = cutn.NetworkOptions(handle=handle, blocking="auto")
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft network opts",mem_avail, "rank =",rank)
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cutn.distributed_reset_configuration(handle, *cutn.get_mpi_comm_pointer(comm))
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft distributed reset config",mem_avail, "rank =",rank)
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# Perform circuit conversion
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myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
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operands_interleave = myconvertor.state_vector_operands()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
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# Pathfinder: To search for the optimal path. Optimal path are assigned to path and info attribute of the network object.
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network = cutn.Network(*operands_interleave, options=network_opts)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft cutn.Network(*operands_interleave,",mem_avail, "rank =",rank)
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network.contract_path(
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optimize={
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"samples": n_samples,
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"threads": ncpu_threads,
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"slicing": {"min_slices": max(16, size)},
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}
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)
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft contract path",mem_avail, "rank =",rank)
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# Execution: To execute the contraction using the optimal path found previously
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# print("opt_cost",opt_info.opt_cost, "Process =",rank)
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"""
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path, opt_info = network.contract_path(optimize={"samples": n_samples, "threads": ncpu_threads, 'slicing': {'min_slices': max(16, size)}})
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num_slices = opt_info.num_slices#Andy
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chunk, extra = num_slices // size, num_slices % size#Andy
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slice_begin = rank * chunk + min(rank, extra)#Andy
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slice_end = num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)#Andy
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slices = range(slice_begin, slice_end)#Andy
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result = network.contract(slices=slices)
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"""
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result = network.contract()
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# mem_avail = cp.cuda.Device().mem_info[0]
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# print("Mem avail: aft contract",mem_avail, "rank =",rank)
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cutn.destroy(handle)
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return result, rank
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def eval_mps(qibo_circ, gate_algo, datatype):
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myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
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mps_helper = MPSContractionHelper(myconvertor.num_qubits)
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return mps_helper.contract_state_vector(
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myconvertor.mps_tensors, {"handle": myconvertor.handle}
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)
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def PauliStringGen(nqubits):
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if nqubits <= 0:
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return "Invalid input. N should be a positive integer."
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# characters = 'IXYZ'
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characters = "XXXZ"
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result = ""
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for i in range(nqubits):
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char_to_add = characters[i % len(characters)]
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result += char_to_add
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return result
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