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Rename function name to be more descriptive [skip CI]

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This commit is contained in:
tankya2
2024-01-30 15:40:11 +08:00
committed by yangliwei
parent a0ad2af0c9
commit 93514a51f6
2 changed files with 11 additions and 9 deletions
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14
src/qibotn/eval.py
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@@ -10,19 +10,19 @@ from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum
from qibotn.QiboCircuitToMPS import QiboCircuitToMPS from qibotn.QiboCircuitToMPS import QiboCircuitToMPS
def eval(qibo_circ, datatype): def dense_vector_tn(qibo_circ, datatype):
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
return contract(*myconvertor.state_vector_operands()) return contract(*myconvertor.state_vector_operands())
def eval_expectation(qibo_circ, datatype): def expectation_tn(qibo_circ, datatype):
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype) myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
return contract( return contract(
*myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits)) *myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
) )
def eval_tn_MPI(qibo_circ, datatype, n_samples=8): 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. """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 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. 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.
@@ -118,7 +118,7 @@ def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
return result, rank return result, rank
def eval_tn_nccl(qibo_circ, datatype, n_samples=8): def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
from mpi4py import MPI # this line initializes MPI from mpi4py import MPI # this line initializes MPI
import socket import socket
from cuquantum import Network from cuquantum import Network
@@ -204,7 +204,7 @@ def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
return result, rank return result, rank
def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8): def expectation_tn_nccl(qibo_circ, datatype, n_samples=8):
from mpi4py import MPI # this line initializes MPI from mpi4py import MPI # this line initializes MPI
import socket import socket
from cuquantum import Network from cuquantum import Network
@@ -291,7 +291,7 @@ def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
return result, rank return result, rank
def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8): def expectation_tn_MPI(qibo_circ, datatype, n_samples=8):
from mpi4py import MPI # this line initializes MPI from mpi4py import MPI # this line initializes MPI
import socket import socket
from cuquantum import Network from cuquantum import Network
@@ -370,7 +370,7 @@ def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
return result, rank return result, rank
def eval_mps(qibo_circ, gate_algo, datatype): def dense_vector_mps(qibo_circ, gate_algo, datatype):
myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype) myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
mps_helper = MPSContractionHelper(myconvertor.num_qubits) mps_helper = MPSContractionHelper(myconvertor.num_qubits)

6
tests/test_cuquantum_cutensor_backend.py
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@@ -39,7 +39,9 @@ def test_eval(nqubits: int, dtype="complex128"):
qibo_time, (qibo_circ, result_sv) = time(lambda: qibo_qft(nqubits, swaps=True)) qibo_time, (qibo_circ, result_sv) = time(lambda: qibo_qft(nqubits, swaps=True))
# Test Cuquantum # Test Cuquantum
cutn_time, result_tn = time(lambda: qibotn.cutn.eval(qibo_circ, dtype).flatten()) cutn_time, result_tn = time(
lambda: qibotn.eval.dense_vector_tn(qibo_circ, dtype).flatten()
)
assert 1e-2 * qibo_time < cutn_time < 1e2 * qibo_time assert 1e-2 * qibo_time < cutn_time < 1e2 * qibo_time
assert np.allclose(result_sv, result_tn), "Resulting dense vectors do not match" assert np.allclose(result_sv, result_tn), "Resulting dense vectors do not match"
@@ -74,7 +76,7 @@ def test_mps(nqubits: int, dtype="complex128"):
} }
cutn_time, result_tn = time( cutn_time, result_tn = time(
lambda: qibotn.eval.eval_mps(circ_qibo, gate_algo, dtype).flatten() lambda: qibotn.eval.dense_vector_mps(circ_qibo, gate_algo, dtype).flatten()
) )
print(f"State vector difference: {abs(result_tn - result_sv_cp).max():0.3e}") print(f"State vector difference: {abs(result_tn - result_sv_cp).max():0.3e}")