353 lines
12 KiB
Python
353 lines
12 KiB
Python
import cupy as cp
|
|
from cupy.cuda.runtime import getDeviceCount
|
|
from cuquantum import contract
|
|
|
|
from qibotn.circuit_convertor import QiboCircuitToEinsum
|
|
from qibotn.circuit_to_mps import QiboCircuitToMPS
|
|
from qibotn.mps_contraction_helper import MPSContractionHelper
|
|
|
|
|
|
def dense_vector_tn(qibo_circ, datatype):
|
|
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
|
|
dense vector."""
|
|
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
|
return contract(*myconvertor.state_vector_operands())
|
|
|
|
|
|
def expectation_pauli_tn(qibo_circ, datatype, pauli_string_pattern):
|
|
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
|
|
expectation of given Pauli string."""
|
|
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
|
return contract(
|
|
*myconvertor.expectation_operands(
|
|
pauli_string_gen(qibo_circ.nqubits, pauli_string_pattern)
|
|
)
|
|
)
|
|
|
|
|
|
def dense_vector_tn_MPI(qibo_circ, datatype, n_samples=8):
|
|
"""Convert qibo circuit to tensornet (TN) format and perform contraction
|
|
using multi node and multi GPU through MPI.
|
|
|
|
The conversion is performed by QiboCircuitToEinsum(), after which it
|
|
goes through 2 steps: pathfinder and execution. 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. After pathfinding the optimal path is used in the actual
|
|
contraction to give a dense vector representation of the TN.
|
|
"""
|
|
|
|
from cuquantum import Network
|
|
from mpi4py import MPI
|
|
|
|
root = 0
|
|
comm = MPI.COMM_WORLD
|
|
rank = comm.Get_rank()
|
|
size = comm.Get_size()
|
|
|
|
device_id = rank % getDeviceCount()
|
|
|
|
# Perform circuit conversion
|
|
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
|
|
|
operands = myconvertor.state_vector_operands()
|
|
|
|
# Assign the device for each process.
|
|
device_id = rank % getDeviceCount()
|
|
|
|
# Create network object.
|
|
network = Network(*operands, options={"device_id": device_id})
|
|
|
|
# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
|
|
path, info = network.contract_path(
|
|
optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}}
|
|
)
|
|
|
|
# Select the best path from all ranks.
|
|
opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
|
|
|
# Broadcast info from the sender to all other ranks.
|
|
info = comm.bcast(info, sender)
|
|
|
|
# Set path and slices.
|
|
path, info = network.contract_path(
|
|
optimize={"path": info.path, "slicing": info.slices}
|
|
)
|
|
|
|
# Calculate this process's share of the slices.
|
|
num_slices = info.num_slices
|
|
chunk, extra = num_slices // size, num_slices % size
|
|
slice_begin = rank * chunk + min(rank, extra)
|
|
slice_end = (
|
|
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
|
)
|
|
slices = range(slice_begin, slice_end)
|
|
|
|
# Contract the group of slices the process is responsible for.
|
|
result = network.contract(slices=slices)
|
|
|
|
# Sum the partial contribution from each process on root.
|
|
result = comm.reduce(sendobj=result, op=MPI.SUM, root=root)
|
|
|
|
return result, rank
|
|
|
|
|
|
def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
|
|
"""Convert qibo circuit to tensornet (TN) format and perform contraction
|
|
using multi node and multi GPU through NCCL.
|
|
|
|
The conversion is performed by QiboCircuitToEinsum(), after which it
|
|
goes through 2 steps: pathfinder and execution. 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. After pathfinding the optimal path is used in the actual
|
|
contraction to give a dense vector representation of the TN.
|
|
"""
|
|
from cupy.cuda import nccl
|
|
from cuquantum import Network
|
|
from mpi4py import MPI
|
|
|
|
root = 0
|
|
comm_mpi = MPI.COMM_WORLD
|
|
rank = comm_mpi.Get_rank()
|
|
size = comm_mpi.Get_size()
|
|
|
|
device_id = rank % getDeviceCount()
|
|
|
|
cp.cuda.Device(device_id).use()
|
|
|
|
# Set up the NCCL communicator.
|
|
nccl_id = nccl.get_unique_id() if rank == root else None
|
|
nccl_id = comm_mpi.bcast(nccl_id, root)
|
|
comm_nccl = nccl.NcclCommunicator(size, nccl_id, rank)
|
|
|
|
# Perform circuit conversion
|
|
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
|
operands = myconvertor.state_vector_operands()
|
|
|
|
network = Network(*operands)
|
|
|
|
# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
|
|
path, info = network.contract_path(
|
|
optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}}
|
|
)
|
|
|
|
# Select the best path from all ranks.
|
|
opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
|
|
|
# Broadcast info from the sender to all other ranks.
|
|
info = comm_mpi.bcast(info, sender)
|
|
|
|
# Set path and slices.
|
|
path, info = network.contract_path(
|
|
optimize={"path": info.path, "slicing": info.slices}
|
|
)
|
|
|
|
# Calculate this process's share of the slices.
|
|
num_slices = info.num_slices
|
|
chunk, extra = num_slices // size, num_slices % size
|
|
slice_begin = rank * chunk + min(rank, extra)
|
|
slice_end = (
|
|
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
|
)
|
|
slices = range(slice_begin, slice_end)
|
|
|
|
# Contract the group of slices the process is responsible for.
|
|
result = network.contract(slices=slices)
|
|
|
|
# Sum the partial contribution from each process on root.
|
|
stream_ptr = cp.cuda.get_current_stream().ptr
|
|
comm_nccl.reduce(
|
|
result.data.ptr,
|
|
result.data.ptr,
|
|
result.size,
|
|
nccl.NCCL_FLOAT64,
|
|
nccl.NCCL_SUM,
|
|
root,
|
|
stream_ptr,
|
|
)
|
|
|
|
return result, rank
|
|
|
|
|
|
def expectation_pauli_tn_nccl(qibo_circ, datatype, pauli_string_pattern, n_samples=8):
|
|
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
|
|
expectation of given Pauli string using multi node and multi GPU through
|
|
NCCL.
|
|
|
|
The conversion is performed by QiboCircuitToEinsum(), after which it
|
|
goes through 2 steps: pathfinder and execution. The
|
|
pauli_string_pattern is used to generate the pauli string
|
|
corresponding to the number of qubits of the system. 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. After pathfinding the optimal path is used in the actual
|
|
contraction to give an expectation value.
|
|
"""
|
|
from cupy.cuda import nccl
|
|
from cuquantum import Network
|
|
from mpi4py import MPI
|
|
|
|
root = 0
|
|
comm_mpi = MPI.COMM_WORLD
|
|
rank = comm_mpi.Get_rank()
|
|
size = comm_mpi.Get_size()
|
|
|
|
device_id = rank % getDeviceCount()
|
|
|
|
cp.cuda.Device(device_id).use()
|
|
|
|
# Set up the NCCL communicator.
|
|
nccl_id = nccl.get_unique_id() if rank == root else None
|
|
nccl_id = comm_mpi.bcast(nccl_id, root)
|
|
comm_nccl = nccl.NcclCommunicator(size, nccl_id, rank)
|
|
|
|
# Perform circuit conversion
|
|
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
|
operands = myconvertor.expectation_operands(
|
|
pauli_string_gen(qibo_circ.nqubits, pauli_string_pattern)
|
|
)
|
|
|
|
network = Network(*operands)
|
|
|
|
# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
|
|
path, info = network.contract_path(
|
|
optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}}
|
|
)
|
|
|
|
# Select the best path from all ranks.
|
|
opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
|
|
|
# Broadcast info from the sender to all other ranks.
|
|
info = comm_mpi.bcast(info, sender)
|
|
|
|
# Set path and slices.
|
|
path, info = network.contract_path(
|
|
optimize={"path": info.path, "slicing": info.slices}
|
|
)
|
|
|
|
# Calculate this process's share of the slices.
|
|
num_slices = info.num_slices
|
|
chunk, extra = num_slices // size, num_slices % size
|
|
slice_begin = rank * chunk + min(rank, extra)
|
|
slice_end = (
|
|
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
|
)
|
|
slices = range(slice_begin, slice_end)
|
|
|
|
# Contract the group of slices the process is responsible for.
|
|
result = network.contract(slices=slices)
|
|
|
|
# Sum the partial contribution from each process on root.
|
|
stream_ptr = cp.cuda.get_current_stream().ptr
|
|
comm_nccl.reduce(
|
|
result.data.ptr,
|
|
result.data.ptr,
|
|
result.size,
|
|
nccl.NCCL_FLOAT64,
|
|
nccl.NCCL_SUM,
|
|
root,
|
|
stream_ptr,
|
|
)
|
|
|
|
return result, rank
|
|
|
|
|
|
def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string_pattern, n_samples=8):
|
|
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
|
|
expectation of given Pauli string using multi node and multi GPU through
|
|
MPI.
|
|
|
|
The conversion is performed by QiboCircuitToEinsum(), after which it
|
|
goes through 2 steps: pathfinder and execution. The
|
|
pauli_string_pattern is used to generate the pauli string
|
|
corresponding to the number of qubits of the system. 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. After pathfinding the optimal path is used in the actual
|
|
contraction to give an expectation value.
|
|
"""
|
|
from cuquantum import Network
|
|
from mpi4py import MPI # this line initializes MPI
|
|
|
|
root = 0
|
|
comm = MPI.COMM_WORLD
|
|
rank = comm.Get_rank()
|
|
size = comm.Get_size()
|
|
|
|
device_id = rank % getDeviceCount()
|
|
|
|
# Perform circuit conversion
|
|
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
|
|
|
operands = myconvertor.expectation_operands(
|
|
pauli_string_gen(qibo_circ.nqubits, pauli_string_pattern)
|
|
)
|
|
|
|
# Assign the device for each process.
|
|
device_id = rank % getDeviceCount()
|
|
|
|
# Create network object.
|
|
network = Network(*operands, options={"device_id": device_id})
|
|
|
|
# Compute the path on all ranks with 8 samples for hyperoptimization. Force slicing to enable parallel contraction.
|
|
path, info = network.contract_path(
|
|
optimize={"samples": n_samples, "slicing": {"min_slices": max(32, size)}}
|
|
)
|
|
|
|
# Select the best path from all ranks.
|
|
opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
|
|
|
# Broadcast info from the sender to all other ranks.
|
|
info = comm.bcast(info, sender)
|
|
|
|
# Set path and slices.
|
|
path, info = network.contract_path(
|
|
optimize={"path": info.path, "slicing": info.slices}
|
|
)
|
|
|
|
# Calculate this process's share of the slices.
|
|
num_slices = info.num_slices
|
|
chunk, extra = num_slices // size, num_slices % size
|
|
slice_begin = rank * chunk + min(rank, extra)
|
|
slice_end = (
|
|
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
|
)
|
|
slices = range(slice_begin, slice_end)
|
|
|
|
# Contract the group of slices the process is responsible for.
|
|
result = network.contract(slices=slices)
|
|
|
|
# Sum the partial contribution from each process on root.
|
|
result = comm.reduce(sendobj=result, op=MPI.SUM, root=root)
|
|
|
|
return result, rank
|
|
|
|
|
|
def dense_vector_mps(qibo_circ, gate_algo, datatype):
|
|
"""Convert qibo circuit to matrix product state (MPS) format and perform
|
|
contraction to dense vector."""
|
|
myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
|
|
mps_helper = MPSContractionHelper(myconvertor.num_qubits)
|
|
|
|
return mps_helper.contract_state_vector(
|
|
myconvertor.mps_tensors, {"handle": myconvertor.handle}
|
|
)
|
|
|
|
|
|
def pauli_string_gen(nqubits, pauli_string_pattern):
|
|
"""Used internally to generate the string based on given pattern and number
|
|
of qubit.
|
|
|
|
Example: pattern: "XZ", number of qubit: 7, output = XZXZXZX
|
|
"""
|
|
if nqubits <= 0:
|
|
return "Invalid input. N should be a positive integer."
|
|
|
|
result = ""
|
|
|
|
for i in range(nqubits):
|
|
char_to_add = pauli_string_pattern[i % len(pauli_string_pattern)]
|
|
result += char_to_add
|
|
return result
|