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profile.py
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profile.py
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# This code is part of Qiskit.
#
# (C) Copyright IBM 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Profile backend options for optimal performance
"""
import numpy as np
from qiskit import transpile, assemble, execute, QuantumRegister, QuantumCircuit
from qiskit.circuit.library import QuantumVolume
from qiskit.quantum_info import random_unitary
from .aererror import AerError
from .backends.aerbackend import AerBackend
from .backends.qasm_simulator import QasmSimulator
def optimize_backend_options(min_qubits=10, max_qubits=25, ntrials=5, circuit=None):
"""Set optimal OpenMP and fusion options for backend."""
# Profile
profile = {}
# Profile OpenMP threshold
parallel_threshold = None
try:
parallel_threshold = profile_parallel_threshold(
min_qubits=min_qubits, max_qubits=max_qubits, circuit=circuit, ntrials=ntrials)
profile['statevector_parallel_threshold'] = parallel_threshold
_set_optimized_option('statevector_parallel_threshold', parallel_threshold)
except AerError:
pass
# Profile CPU and GPU fusion threshold
for gpu in (False, True):
postfix = '_gpu' if gpu else ''
try:
fusion_threshold = profile_fusion_threshold(gpu=gpu,
min_qubits=min_qubits,
max_qubits=max_qubits,
ntrials=ntrials,
circuit=circuit)
profile[f'fusion_threshold{postfix}'] = fusion_threshold
_set_optimized_option(f'fusion_threshold{postfix}', fusion_threshold)
default_qubit = 20
num_qubits = min(max(default_qubit, fusion_threshold), max_qubits)
costs = profile_fusion_costs(num_qubits,
ntrials=ntrials,
gpu=gpu,
diagonal=False)
for i, _ in enumerate(costs):
profile[f'fusion_cost{postfix}.{i + 1}'] = costs[i]
_set_optimized_option(f'fusion_cost{postfix}.{i + 1}', costs[i])
except AerError:
pass
return profile
def _set_optimized_option(option_name, value):
setattr(AerBackend, f'_{option_name}', value)
def clear_optimized_backend_options():
"""Set profiled options for backend."""
_clear_optimized_option('statevector_parallel_threshold')
for gpu in (False, True):
postfix = '_gpu' if gpu else ''
_clear_optimized_option(f'fusion_threshold{postfix}')
for i in range(5):
_clear_optimized_option(f'fusion_cost{postfix}.{i + 1}')
def _clear_optimized_option(option_name):
if hasattr(AerBackend, f'_{option_name}'):
delattr(AerBackend, f'_{option_name}')
def _generate_profile_circuit(profile_qubit, base_circuit=None, basis_gates=None):
if profile_qubit < 3:
raise AerError(f'number of qubit is too small: {profile_qubit}')
if basis_gates is None:
basis_gates = ['id', 'u', 'cx']
if base_circuit is None:
return transpile(QuantumVolume(profile_qubit, 10), basis_gates=basis_gates)
profile_circuit = transpile(base_circuit.copy(), basis_gates=basis_gates)
if profile_qubit < profile_circuit.num_qubits:
def global_index(qubit):
ret = 0
for qreg in profile_circuit.qregs:
if qreg is qubit.register:
return ret + qubit.index
else:
ret += qreg.size
raise ValueError(f'odd qubit: {qubit}')
def global_qubit(index):
orig = index
for qreg in profile_circuit.qregs:
if index < qreg.size:
return qreg[index]
else:
index -= qreg.size
raise ValueError(f'odd index: {orig}')
for i in range(len(profile_circuit.data)):
inst, qubits, cbits = profile_circuit.data[i]
new_qubit_idxs = []
changed = False
for qubit in qubits:
gidx = global_index(qubit) % profile_qubit
while gidx in new_qubit_idxs:
gidx = np.random.randint(profile_qubit)
changed |= (gidx != global_index(qubit))
new_qubit_idxs.append(gidx)
if changed:
profile_circuit.data[i] = (inst,
[global_qubit(idx) for idx in new_qubit_idxs],
cbits)
elif profile_circuit.num_qubits < profile_qubit:
# add new qubits
new_register = QuantumRegister(profile_qubit - profile_circuit.num_qubits)
profile_circuit.add_register(new_register)
# add a gate for each new qubit to prevent truncation
for new_qubit in new_register:
profile_circuit.u(0, 1, 2, new_qubit)
return profile_circuit
def _profile_run(simulator, ntrials, backend_options,
qubit, circuit, basis_gates=None):
if basis_gates is None:
basis_gates = ['id', 'u', 'cx']
profile_circuit = _generate_profile_circuit(qubit, circuit, basis_gates)
qobj = assemble(ntrials * [profile_circuit], shots=1)
result = simulator.run(qobj, **backend_options).result()
time_taken = 0.0
for j in range(ntrials):
time_taken += result.results[j].time_taken
return time_taken
def profile_parallel_threshold(min_qubits=10, max_qubits=20, ntrials=10,
circuit=None, backend_options=None, return_ratios=False):
"""Evaluate optimal OMP parallel threshold for current system."""
profile_opts = {'method': 'statevector',
'max_parallel_experiments': 1,
'max_parallel_shots': 1,
'fusion_enabled': False}
if backend_options is not None:
for key, val in backend_options.items():
profile_opts[key] = val
simulator = QasmSimulator()
ratios = []
for qubit in range(min_qubits, max_qubits + 1):
profile_opts['statevector_parallel_threshold'] = 64
serial_time_taken = _profile_run(simulator, ntrials, profile_opts, qubit, circuit)
profile_opts['statevector_parallel_threshold'] = 1
parallel_time_taken = _profile_run(simulator, ntrials, profile_opts, qubit, circuit)
if return_ratios:
ratios.append(serial_time_taken / parallel_time_taken)
elif serial_time_taken < parallel_time_taken:
return qubit
if return_ratios:
return ratios
raise AerError(f'Unable to find threshold in range [{min_qubits}, {max_qubits}]')
def profile_fusion_threshold(min_qubits=10, max_qubits=20, ntrials=10,
circuit=None, backend_options=None, gpu=False,
return_ratios=False):
"""Evaluate optimal OMP parallel threshold for current system."""
profile_opts = {'method': 'statevector',
'max_parallel_experiments': 1,
'max_parallel_shots': 1,
'fusion_threshold': 1}
if backend_options is not None:
for key, val in backend_options.items():
profile_opts[key] = val
simulator = QasmSimulator()
# Ensure fusion is enabled and method is statevector
if gpu:
# Check GPU is supported
result = execute(QuantumVolume(1), simulator, method='statevector_gpu').result()
if not result.success:
raise AerError('"statevector_gpu" backend is not supported on this system.')
profile_opts['method'] = 'statevector_gpu'
ratios = []
for qubit in range(min_qubits, max_qubits + 1):
profile_opts['fusion_enabled'] = False
non_fusion_time_taken = _profile_run(simulator, ntrials, profile_opts, qubit, circuit)
profile_opts['fusion_enabled'] = True
fusion_time_taken = _profile_run(simulator, ntrials, profile_opts, qubit, circuit)
if return_ratios:
ratios.append(non_fusion_time_taken / fusion_time_taken)
elif non_fusion_time_taken < fusion_time_taken:
return qubit
if return_ratios:
return ratios
raise AerError(f'Unable to find threshold in range [{min_qubits}, {max_qubits}]')
def profile_fusion_costs(num_qubits, ntrials=10, backend_options=None, gpu=False, diagonal=False):
"""Evaluate optimal costs in cost-based fusion for current system."""
profile_opts = {'method': 'statevector',
'max_parallel_experiments': 1,
'max_parallel_shots': 1,
'fusion_threshold': 1}
if backend_options is not None:
for key, val in backend_options.items():
profile_opts[key] = val
simulator = QasmSimulator()
# Ensure fusion is enabled and method is statevector
if gpu:
# Check GPU is supported
result = execute(QuantumVolume(1), simulator, method='statevector_gpu').result()
if not result.success:
raise AerError('"statevector_gpu" backend is not supported on this system.')
profile_opts['method'] = 'statevector_gpu'
basis_gates = ['id', 'u', 'cx', 'unitary', 'diagonal']
all_gate_time = []
for target in range(0, 5):
# Generate a circuit that consists of only unitary/diagonal gates with target-qubit
profile_circuit = QuantumCircuit(num_qubits)
if diagonal:
for i in range(0, 100):
qubits = [q % num_qubits for q in range(i, i + target + 1)]
profile_circuit.diagonal([1, -1] * (2 ** target), qubits)
else:
for i in range(0, 100):
qubits = [q % num_qubits for q in range(i, i + target + 1)]
profile_circuit.unitary(random_unitary(2 ** (target + 1)), qubits)
all_gate_time.append(_profile_run(simulator,
ntrials,
profile_opts,
num_qubits,
profile_circuit,
basis_gates))
costs = []
for target in range(0, 5):
costs.append(all_gate_time[target] / all_gate_time[0])
return costs