Format with black
This commit is contained in:
tankya2
@@ -13,9 +13,13 @@ def eval(qibo_circ, datatype):
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
return contract(*myconvertor.state_vector_operands())
|
||||
|
||||
|
||||
def eval_expectation(qibo_circ, datatype):
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
return contract(*myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits)))
|
||||
return contract(
|
||||
*myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
|
||||
)
|
||||
|
||||
|
||||
def eval_tn_MPI_2(qibo_circ, datatype, n_samples=8):
|
||||
from mpi4py import MPI # this line initializes MPI
|
||||
@@ -23,73 +27,79 @@ def eval_tn_MPI_2(qibo_circ, datatype, n_samples=8):
|
||||
from cuquantum import Network
|
||||
|
||||
# Get the hostname
|
||||
#hostname = socket.gethostname()
|
||||
|
||||
# hostname = socket.gethostname()
|
||||
|
||||
root = 0
|
||||
comm = MPI.COMM_WORLD
|
||||
rank = comm.Get_rank()
|
||||
size = comm.Get_size()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
device_id = rank % getDeviceCount()
|
||||
|
||||
|
||||
|
||||
# Perform circuit conversion
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
operands = myconvertor.state_vector_operands()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
# Broadcast the operand data.
|
||||
#operands = comm.bcast(operands, root)
|
||||
|
||||
# operands = comm.bcast(operands, root)
|
||||
|
||||
# Assign the device for each process.
|
||||
device_id = rank % getDeviceCount()
|
||||
|
||||
#dev = cp.cuda.Device(device_id)
|
||||
#free_mem, total_mem = dev.mem_info
|
||||
#print("Mem free: ",free_mem, "Total mem: ",total_mem, "rank =",rank)
|
||||
|
||||
# dev = cp.cuda.Device(device_id)
|
||||
# free_mem, total_mem = dev.mem_info
|
||||
# print("Mem free: ",free_mem, "Total mem: ",total_mem, "rank =",rank)
|
||||
|
||||
# Create network object.
|
||||
network = Network(*operands, options={'device_id' : device_id})
|
||||
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': 8, 'slicing': {'min_slices': max(32, size)}})
|
||||
#print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
path, info = network.contract_path(
|
||||
optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
|
||||
)
|
||||
# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
|
||||
# Select the best path from all ranks.
|
||||
opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
||||
|
||||
#if rank == root:
|
||||
# if rank == root:
|
||||
# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
|
||||
|
||||
# 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})
|
||||
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)
|
||||
slice_end = (
|
||||
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
||||
)
|
||||
slices = range(slice_begin, slice_end)
|
||||
|
||||
#print(f"Process {rank} is processing slice range: {slices}.")
|
||||
# print(f"Process {rank} is processing slice range: {slices}.")
|
||||
|
||||
# Contract the group of slices the process is responsible for.
|
||||
result = network.contract(slices=slices)
|
||||
#print(f"Process {rank} result shape is : {result.shape}.")
|
||||
#print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
# print(f"Process {rank} result shape is : {result.shape}.")
|
||||
# print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
|
||||
# Sum the partial contribution from each process on root.
|
||||
result = comm.reduce(sendobj=result, op=MPI.SUM, root=root)
|
||||
|
||||
|
||||
return result, rank
|
||||
|
||||
|
||||
def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
|
||||
from mpi4py import MPI # this line initializes MPI
|
||||
import socket
|
||||
@@ -97,18 +107,18 @@ def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
|
||||
from cupy.cuda import nccl
|
||||
|
||||
# Get the hostname
|
||||
#hostname = socket.gethostname()
|
||||
|
||||
# hostname = socket.gethostname()
|
||||
|
||||
root = 0
|
||||
comm_mpi = MPI.COMM_WORLD
|
||||
rank = comm_mpi.Get_rank()
|
||||
size = comm_mpi.Get_size()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
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)
|
||||
@@ -116,51 +126,66 @@ def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
|
||||
|
||||
# Perform circuit conversion
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
operands = myconvertor.state_vector_operands()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
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': 8, 'slicing': {'min_slices': max(32, size)}})
|
||||
path, info = network.contract_path(
|
||||
optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
|
||||
)
|
||||
|
||||
#print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
|
||||
# Select the best path from all ranks.
|
||||
opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
||||
|
||||
#if rank == root:
|
||||
# if rank == root:
|
||||
# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
|
||||
|
||||
# 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})
|
||||
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)
|
||||
slice_end = (
|
||||
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
||||
)
|
||||
slices = range(slice_begin, slice_end)
|
||||
|
||||
#print(f"Process {rank} is processing slice range: {slices}.")
|
||||
# print(f"Process {rank} is processing slice range: {slices}.")
|
||||
|
||||
# Contract the group of slices the process is responsible for.
|
||||
result = network.contract(slices=slices)
|
||||
#print(f"Process {rank} result shape is : {result.shape}.")
|
||||
#print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
# print(f"Process {rank} result shape is : {result.shape}.")
|
||||
# print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
|
||||
# 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)
|
||||
|
||||
comm_nccl.reduce(
|
||||
result.data.ptr,
|
||||
result.data.ptr,
|
||||
result.size,
|
||||
nccl.NCCL_FLOAT64,
|
||||
nccl.NCCL_SUM,
|
||||
root,
|
||||
stream_ptr,
|
||||
)
|
||||
|
||||
return result, rank
|
||||
|
||||
|
||||
def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
|
||||
from mpi4py import MPI # this line initializes MPI
|
||||
import socket
|
||||
@@ -168,18 +193,18 @@ def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
|
||||
from cupy.cuda import nccl
|
||||
|
||||
# Get the hostname
|
||||
#hostname = socket.gethostname()
|
||||
|
||||
# hostname = socket.gethostname()
|
||||
|
||||
root = 0
|
||||
comm_mpi = MPI.COMM_WORLD
|
||||
rank = comm_mpi.Get_rank()
|
||||
size = comm_mpi.Get_size()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
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)
|
||||
@@ -187,50 +212,64 @@ def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
|
||||
|
||||
# Perform circuit conversion
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
operands = myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
|
||||
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
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': 8, 'slicing': {'min_slices': max(32, size)}})
|
||||
path, info = network.contract_path(
|
||||
optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
|
||||
)
|
||||
|
||||
#print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
|
||||
# Select the best path from all ranks.
|
||||
opt_cost, sender = comm_mpi.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
||||
|
||||
#if rank == root:
|
||||
# if rank == root:
|
||||
# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
|
||||
|
||||
# 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})
|
||||
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)
|
||||
slice_end = (
|
||||
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
||||
)
|
||||
slices = range(slice_begin, slice_end)
|
||||
|
||||
#print(f"Process {rank} is processing slice range: {slices}.")
|
||||
# print(f"Process {rank} is processing slice range: {slices}.")
|
||||
|
||||
# Contract the group of slices the process is responsible for.
|
||||
result = network.contract(slices=slices)
|
||||
#print(f"Process {rank} result shape is : {result.shape}.")
|
||||
#print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
# print(f"Process {rank} result shape is : {result.shape}.")
|
||||
# print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
|
||||
# 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)
|
||||
|
||||
comm_nccl.reduce(
|
||||
result.data.ptr,
|
||||
result.data.ptr,
|
||||
result.size,
|
||||
nccl.NCCL_FLOAT64,
|
||||
nccl.NCCL_SUM,
|
||||
root,
|
||||
stream_ptr,
|
||||
)
|
||||
|
||||
return result, rank
|
||||
|
||||
|
||||
@@ -240,128 +279,144 @@ def eval_tn_MPI_2_expectation(qibo_circ, datatype, n_samples=8):
|
||||
from cuquantum import Network
|
||||
|
||||
# Get the hostname
|
||||
#hostname = socket.gethostname()
|
||||
|
||||
# hostname = socket.gethostname()
|
||||
|
||||
root = 0
|
||||
comm = MPI.COMM_WORLD
|
||||
rank = comm.Get_rank()
|
||||
size = comm.Get_size()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
device_id = rank % getDeviceCount()
|
||||
|
||||
|
||||
|
||||
# Perform circuit conversion
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
operands = myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
# Broadcast the operand data.
|
||||
#operands = comm.bcast(operands, root)
|
||||
|
||||
# operands = comm.bcast(operands, root)
|
||||
|
||||
# Assign the device for each process.
|
||||
device_id = rank % getDeviceCount()
|
||||
|
||||
#dev = cp.cuda.Device(device_id)
|
||||
#free_mem, total_mem = dev.mem_info
|
||||
#print("Mem free: ",free_mem, "Total mem: ",total_mem, "rank =",rank)
|
||||
|
||||
# dev = cp.cuda.Device(device_id)
|
||||
# free_mem, total_mem = dev.mem_info
|
||||
# print("Mem free: ",free_mem, "Total mem: ",total_mem, "rank =",rank)
|
||||
|
||||
# Create network object.
|
||||
network = Network(*operands, options={'device_id' : device_id})
|
||||
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': 8, 'slicing': {'min_slices': max(32, size)}})
|
||||
#print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
path, info = network.contract_path(
|
||||
optimize={"samples": 8, "slicing": {"min_slices": max(32, size)}}
|
||||
)
|
||||
# print(f"Process {rank} has the path with the FLOP count {info.opt_cost}.")
|
||||
|
||||
# Select the best path from all ranks.
|
||||
opt_cost, sender = comm.allreduce(sendobj=(info.opt_cost, rank), op=MPI.MINLOC)
|
||||
|
||||
#if rank == root:
|
||||
# if rank == root:
|
||||
# print(f"Process {sender} has the path with the lowest FLOP count {opt_cost}.")
|
||||
|
||||
# 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})
|
||||
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)
|
||||
slice_end = (
|
||||
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
||||
)
|
||||
slices = range(slice_begin, slice_end)
|
||||
|
||||
#print(f"Process {rank} is processing slice range: {slices}.")
|
||||
# print(f"Process {rank} is processing slice range: {slices}.")
|
||||
|
||||
# Contract the group of slices the process is responsible for.
|
||||
result = network.contract(slices=slices)
|
||||
#print(f"Process {rank} result shape is : {result.shape}.")
|
||||
#print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
# print(f"Process {rank} result shape is : {result.shape}.")
|
||||
# print(f"Process {rank} result size is : {result.nbytes}.")
|
||||
|
||||
# Sum the partial contribution from each process on root.
|
||||
result = comm.reduce(sendobj=result, op=MPI.SUM, root=root)
|
||||
|
||||
|
||||
return result, rank
|
||||
|
||||
|
||||
def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
|
||||
from mpi4py import MPI # this line initializes MPI
|
||||
import socket
|
||||
|
||||
# Get the hostname
|
||||
#hostname = socket.gethostname()
|
||||
|
||||
# hostname = socket.gethostname()
|
||||
|
||||
ncpu_threads = multiprocessing.cpu_count() // 2
|
||||
|
||||
|
||||
comm = MPI.COMM_WORLD
|
||||
rank = comm.Get_rank()
|
||||
size = comm.Get_size()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
device_id = rank % getDeviceCount()
|
||||
cp.cuda.Device(device_id).use()
|
||||
|
||||
handle = cutn.create()
|
||||
network_opts = cutn.NetworkOptions(handle=handle, blocking="auto")
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft network opts",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft network opts",mem_avail, "rank =",rank)
|
||||
cutn.distributed_reset_configuration(handle, *cutn.get_mpi_comm_pointer(comm))
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft distributed reset config",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft distributed reset config",mem_avail, "rank =",rank)
|
||||
# Perform circuit conversion
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
operands_interleave = myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
operands_interleave = myconvertor.expectation_operands(
|
||||
PauliStringGen(qibo_circ.nqubits)
|
||||
)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
# Pathfinder: To search for the optimal path. Optimal path are assigned to path and info attribute of the network object.
|
||||
network = cutn.Network(*operands_interleave, options=network_opts)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft cutn.Network(*operands_interleave,",mem_avail, "rank =",rank)
|
||||
path, opt_info = network.contract_path(optimize={"samples": n_samples, "threads": ncpu_threads, 'slicing': {'min_slices': max(16, size)}})
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft contract path",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft cutn.Network(*operands_interleave,",mem_avail, "rank =",rank)
|
||||
path, opt_info = network.contract_path(
|
||||
optimize={
|
||||
"samples": n_samples,
|
||||
"threads": ncpu_threads,
|
||||
"slicing": {"min_slices": max(16, size)},
|
||||
}
|
||||
)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft contract path",mem_avail, "rank =",rank)
|
||||
# Execution: To execute the contraction using the optimal path found previously
|
||||
#print("opt_cost",opt_info.opt_cost, "Process =",rank)
|
||||
# print("opt_cost",opt_info.opt_cost, "Process =",rank)
|
||||
|
||||
|
||||
num_slices = opt_info.num_slices#Andy
|
||||
chunk, extra = num_slices // size, num_slices % size#Andy
|
||||
slice_begin = rank * chunk + min(rank, extra)#Andy
|
||||
slice_end = num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)#Andy
|
||||
slices = range(slice_begin, slice_end)#Andy
|
||||
num_slices = opt_info.num_slices # Andy
|
||||
chunk, extra = num_slices // size, num_slices % size # Andy
|
||||
slice_begin = rank * chunk + min(rank, extra) # Andy
|
||||
slice_end = (
|
||||
num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)
|
||||
) # Andy
|
||||
slices = range(slice_begin, slice_end) # Andy
|
||||
result = network.contract(slices=slices)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft contract",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft contract",mem_avail, "rank =",rank)
|
||||
cutn.destroy(handle)
|
||||
|
||||
return result, rank
|
||||
|
||||
|
||||
def eval_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.
|
||||
@@ -371,45 +426,52 @@ def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
|
||||
|
||||
from mpi4py import MPI # this line initializes MPI
|
||||
import socket
|
||||
|
||||
# Get the hostname
|
||||
#hostname = socket.gethostname()
|
||||
|
||||
# hostname = socket.gethostname()
|
||||
|
||||
ncpu_threads = multiprocessing.cpu_count() // 2
|
||||
|
||||
|
||||
comm = MPI.COMM_WORLD
|
||||
rank = comm.Get_rank()
|
||||
size = comm.Get_size()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: Start",mem_avail, "rank =",rank, "hostname =",hostname)
|
||||
device_id = rank % getDeviceCount()
|
||||
cp.cuda.Device(device_id).use()
|
||||
|
||||
handle = cutn.create()
|
||||
network_opts = cutn.NetworkOptions(handle=handle, blocking="auto")
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft network opts",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft network opts",mem_avail, "rank =",rank)
|
||||
cutn.distributed_reset_configuration(handle, *cutn.get_mpi_comm_pointer(comm))
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft distributed reset config",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft distributed reset config",mem_avail, "rank =",rank)
|
||||
# Perform circuit conversion
|
||||
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft convetor",mem_avail, "rank =",rank)
|
||||
operands_interleave = myconvertor.state_vector_operands()
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft operand interleave",mem_avail, "rank =",rank)
|
||||
|
||||
# Pathfinder: To search for the optimal path. Optimal path are assigned to path and info attribute of the network object.
|
||||
network = cutn.Network(*operands_interleave, options=network_opts)
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft cutn.Network(*operands_interleave,",mem_avail, "rank =",rank)
|
||||
network.contract_path(optimize={"samples": n_samples, "threads": ncpu_threads, 'slicing': {'min_slices': max(16, size)}})
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft contract path",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft cutn.Network(*operands_interleave,",mem_avail, "rank =",rank)
|
||||
network.contract_path(
|
||||
optimize={
|
||||
"samples": n_samples,
|
||||
"threads": ncpu_threads,
|
||||
"slicing": {"min_slices": max(16, size)},
|
||||
}
|
||||
)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft contract path",mem_avail, "rank =",rank)
|
||||
# Execution: To execute the contraction using the optimal path found previously
|
||||
#print("opt_cost",opt_info.opt_cost, "Process =",rank)
|
||||
# print("opt_cost",opt_info.opt_cost, "Process =",rank)
|
||||
|
||||
'''
|
||||
"""
|
||||
path, opt_info = network.contract_path(optimize={"samples": n_samples, "threads": ncpu_threads, 'slicing': {'min_slices': max(16, size)}})
|
||||
|
||||
num_slices = opt_info.num_slices#Andy
|
||||
@@ -418,16 +480,16 @@ def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
|
||||
slice_end = num_slices if rank == size - 1 else (rank + 1) * chunk + min(rank + 1, extra)#Andy
|
||||
slices = range(slice_begin, slice_end)#Andy
|
||||
result = network.contract(slices=slices)
|
||||
'''
|
||||
"""
|
||||
result = network.contract()
|
||||
|
||||
#mem_avail = cp.cuda.Device().mem_info[0]
|
||||
#print("Mem avail: aft contract",mem_avail, "rank =",rank)
|
||||
# mem_avail = cp.cuda.Device().mem_info[0]
|
||||
# print("Mem avail: aft contract",mem_avail, "rank =",rank)
|
||||
cutn.destroy(handle)
|
||||
|
||||
return result, rank
|
||||
|
||||
|
||||
|
||||
def eval_mps(qibo_circ, gate_algo, datatype):
|
||||
myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
|
||||
mps_helper = MPSContractionHelper(myconvertor.num_qubits)
|
||||
@@ -436,18 +498,18 @@ def eval_mps(qibo_circ, gate_algo, datatype):
|
||||
myconvertor.mps_tensors, {"handle": myconvertor.handle}
|
||||
)
|
||||
|
||||
|
||||
def PauliStringGen(nqubits):
|
||||
|
||||
if nqubits <= 0:
|
||||
return "Invalid input. N should be a positive integer."
|
||||
|
||||
#characters = 'IXYZ'
|
||||
characters = 'XXXZ'
|
||||
# characters = 'IXYZ'
|
||||
characters = "XXXZ"
|
||||
|
||||
result = ''
|
||||
result = ""
|
||||
|
||||
for i in range(nqubits):
|
||||
char_to_add = characters[i % len(characters)]
|
||||
result += char_to_add
|
||||
|
||||
return result
|
||||
return result
|
||||
|
||||
Reference in New Issue
Block a user