Remove eval_tn_mpi

src/qibotn/cutn.py

@@ -433,80 +433,6 @@ def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
 
     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.
-    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 mpi4py import MPI  # this line initializes MPI
-    import socket
-
-    # Get the hostname
-    # 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)
-    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)
-    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)
-    # 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)
-    operands_interleave = myconvertor.state_vector_operands()
-    # 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)
-    # Execution: To execute the contraction using the optimal path found previously
-    # 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
-    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)
-    """
-    result = network.contract()
-
-    # 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)
@@ -515,7 +441,6 @@ 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."