added draft code for multi node

src/qibotn/test_multinode.py

@@ -0,0 +1,126 @@
+import qibo
+#import qibotn.cutn as cutn
+from cuquantum import cutensornet as cutn
+
+from qibo import gates
+from qibo.models import Circuit, QFT
+import numpy as np
+from mpi4py import MPI  # this line initializes MPI
+import cupy as cp
+from cupy.cuda.runtime import getDeviceCount
+from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum
+import cuquantum
+
+def qibo_qft(nqubits, swaps):
+    circ_qibo = QFT(nqubits, swaps)
+    state_vec = np.array(circ_qibo())
+    return circ_qibo, state_vec
+
+print("QiboTN")
+
+root = 0
+comm = MPI.COMM_WORLD
+rank, size = comm.Get_rank(), comm.Get_size()
+print("Andy: Rank ", rank," size ", size)
+# Assign the device for each process.
+device_id = rank % getDeviceCount()
+cp.cuda.Device(device_id).use()
+
+datatype = 'complex128'
+nqubits = 10
+'''
+qibo_circ = Circuit(nqubits)
+qibo_circ.add(gates.H(0))
+#qibo_circ.add(gates.CZ(3,4))
+qibo_circ.add(gates.CZ(2,4))
+#qibo_circ.add(gates.CNOT(0,4))
+#qibo_circ.add(gates.SWAP(0,4))
+qibo_circ.add(gates.H(2))
+qibo_circ.add(gates.H(4))
+'''
+qibo_circ = QFT(nqubits)
+
+'''
+expr = 'ehl,gj,edhg,bif,d,c,k,iklj,cf,a->ba'
+shapes = [(8, 2, 5), (5, 7), (8, 8, 2, 5), (8, 6, 3), (8,), (6,), (5,), (6, 5, 5, 7), (6, 3), (3,)]
+print("Andy: expr =",expr)
+if rank == root:
+    operands = [cp.random.rand(*shape) for shape in shapes]
+else:
+    operands = [cp.empty(shape) for shape in shapes]
+'''
+
+myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
+expr, mode_label, q_frontier, operands = myconvertor.state_vector()
+shapes = [tensor.shape for tensor in operands]
+print("expr ", expr)
+print("Operands ", operands)
+print("Shape", shapes)
+# Set the operand data on root. Since we use the buffer interface APIs offered by mpi4py for communicating array
+#  objects, we can directly use device arrays (cupy.ndarray, for example) as we assume mpi4py is built against
+#  a CUDA-aware MPI.
+if rank != root:
+    operands = [cp.empty(shape,dtype="complex128") for shape in shapes]
+
+'''    
+if rank == root:
+    operands = [cp.random.rand(*shape) for shape in shapes]
+    print("Operands random", operands)
+
+else:
+    operands = [cp.empty(shape) for shape in shapes]
+'''
+
+for operand in operands:
+    print("Is CUPY array? ", cp.get_array_module(operand), " Operand size = ", operand.nbytes)
+
+for operand in operands:
+    comm.Bcast(operand, root)
+
+# Bind the communicator to the library handle
+handle = cutn.create()
+print("Andy cutn.create()")
+print("Andy ", cutn.get_mpi_comm_pointer(comm))
+cutn.distributed_reset_configuration(
+    handle, *cutn.get_mpi_comm_pointer(comm)
+)
+print("Andy cutn.distributed_reset_configuration")
+
+operands_interleave = myconvertor.get_interleave_format( mode_label, q_frontier, operands)
+print("new function interkeave ", operands_interleave)
+print("Ori function interleave", myconvertor.state_vector_operands())
+
+result = cuquantum.contract(*operands_interleave, options={'device_id' : device_id, 'handle': handle})
+#result = cuquantum.contract(expr, *operands, options={'device_id' : device_id, 'handle': handle})
+
+'''
+
+# Create a new GPU buffer for verification
+result_cp = cp.empty_like(result)
+
+# Sum the partial contribution from each process on root, with GPU
+if rank == root:
+    comm.Reduce(sendbuf=MPI.IN_PLACE, recvbuf=result_cp, op=MPI.SUM, root=root)
+else:
+    comm.Reduce(sendbuf=result_cp, recvbuf=None, op=MPI.SUM, root=root)
+'''
+# Check correctness.
+if rank == root:
+    #operands = myconvertor.state_vector_operands()
+    #result_cp = cp.einsum(*operands, optimize=True)
+    #result_cp = np.einsum(*operands, optimize=True)
+    (qibo_circ, result_sv) =  qibo_qft(nqubits, swaps=True)
+    print("Does the cuQuantum parallel contraction result match the cupy.einsum result?", cp.allclose(result.flatten(), result_sv))
+
+
+'''
+result_tn = cutn.eval(qibo_circ, datatype)
+
+qibo.set_backend(backend="qibojit", platform="numpy")
+(qibo_circ, result_sv) = qibo_qft(nqubits, swaps=True)
+#print(result_tn)
+#print(result_sv)
+
+assert np.allclose(
+        result_sv, result_tn.flatten()), "Resulting dense vectors do not match"
+'''