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Use runcard to select compute type as no other way to pass in para wout changing struct

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This commit is contained in:
tankya2
2024-01-31 10:52:09 +08:00
committed by yangliwei
parent c43170ffa4
commit 182dc4b00f
1 changed files with 68 additions and 62 deletions
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130
src/qibotn/backends/gpu.py
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@@ -8,11 +8,24 @@ from qibo.config import raise_error
class CuTensorNet(NumpyBackend): # pragma: no cover
# CI does not test for GPU
def __init__(self):
def __init__(self, runcard):
super().__init__()
import cuquantum # pylint: disable=import-error
from cuquantum import cutensornet as cutn # pylint: disable=import-error
if runcard is not None:
print("inside runcard")
# Parse the runcard or use its values to set flags
self.MPI_enabled = runcard.get("MPI_enabled", False)
self.MPS_enabled = runcard.get("MPS_enabled", False)
self.NCCL_enabled = runcard.get("NCCL_enabled", False)
self.expectation_enabled = runcard.get("expectation_enabled", False)
else:
self.MPI_enabled = False
self.MPS_enabled = False
self.NCCL_enabled = False
self.expectation_enabled = False
self.name = "qibotn"
self.cuquantum = cuquantum
self.cutn = cutn
@@ -53,7 +66,7 @@ class CuTensorNet(NumpyBackend): # pragma: no cover
raise TypeError("Type can be either complex64 or complex128")
def execute_circuit(
self, circuit, MPI_enabled=False, MPS_enabled=False, NCCL_enabled=False, expectation_enabled=False, initial_state=None, nshots=None, return_array=False
self, circuit, initial_state=None, nshots=None, return_array=False
): # pragma: no cover
"""Executes a quantum circuit.
@@ -68,32 +81,31 @@ class CuTensorNet(NumpyBackend): # pragma: no cover
"""
import qibotn.eval as eval
print("MPI_enabled", MPI_enabled)
print("MPS_enabled", MPS_enabled)
print("NCCL_enabled", NCCL_enabled)
print("expectation_enabled", expectation_enabled)
print("MPI_enabled", self.MPI_enabled)
print("MPS_enabled", self.MPS_enabled)
print("NCCL_enabled", self.NCCL_enabled)
print("expectation_enabled", self.expectation_enabled)
if (
MPI_enabled == False
and MPS_enabled == False
and NCCL_enabled == False
and expectation_enabled == False
self.MPI_enabled == False
and self.MPS_enabled == False
and self.NCCL_enabled == False
and self.expectation_enabled == False
):
if initial_state is not None:
raise_error(NotImplementedError,
"QiboTN cannot support initial state.")
raise_error(NotImplementedError, "QiboTN cannot support initial state.")
state = eval.dense_vector_tn(circuit, self.dtype)
if (
MPI_enabled == False
and MPS_enabled == True
and NCCL_enabled == False
and expectation_enabled == False
elif (
self.MPI_enabled == False
and self.MPS_enabled == True
and self.NCCL_enabled == False
and self.expectation_enabled == False
):
if initial_state is not None:
raise_error(NotImplementedError,
"QiboTN cannot support initial state.")
raise_error(NotImplementedError, "QiboTN cannot support initial state.")
gate_algo = {
"qr_method": False,
@@ -104,81 +116,75 @@ class CuTensorNet(NumpyBackend): # pragma: no cover
} # make this user input
state = eval.dense_vector_mps(circuit, gate_algo, self.dtype)
if (
MPI_enabled == True
and MPS_enabled == False
and NCCL_enabled == False
and expectation_enabled == False
elif (
self.MPI_enabled == True
and self.MPS_enabled == False
and self.NCCL_enabled == False
and self.expectation_enabled == False
):
if initial_state is not None:
raise_error(NotImplementedError,
"QiboTN cannot support initial state.")
raise_error(NotImplementedError, "QiboTN cannot support initial state.")
state, rank = eval.dense_vector_tn_MPI(circuit, self.dtype, 32)
if rank > 0:
state = np.array(0)
if (
MPI_enabled == False
and MPS_enabled == False
and NCCL_enabled == True
and expectation_enabled == False
elif (
self.MPI_enabled == False
and self.MPS_enabled == False
and self.NCCL_enabled == True
and self.expectation_enabled == False
):
if initial_state is not None:
raise_error(NotImplementedError,
"QiboTN cannot support initial state.")
raise_error(NotImplementedError, "QiboTN cannot support initial state.")
state, rank = eval.dense_vector_tn_nccl(circuit, self.dtype, 32)
if rank > 0:
state = np.array(0)
if (
MPI_enabled == False
and MPS_enabled == False
and NCCL_enabled == False
and expectation_enabled == True
elif (
self.MPI_enabled == False
and self.MPS_enabled == False
and self.NCCL_enabled == False
and self.expectation_enabled == True
):
if initial_state is not None:
raise_error(NotImplementedError,
"QiboTN cannot support initial state.")
raise_error(NotImplementedError, "QiboTN cannot support initial state.")
state = eval.expectation_tn(circuit, self.dtype)
state = eval.expectation_pauli_tn(circuit, self.dtype)
if (
MPI_enabled == True
and MPS_enabled == False
and NCCL_enabled == False
and expectation_enabled == True
elif (
self.MPI_enabled == True
and self.MPS_enabled == False
and self.NCCL_enabled == False
and self.expectation_enabled == True
):
if initial_state is not None:
raise_error(NotImplementedError,
"QiboTN cannot support initial state.")
raise_error(NotImplementedError, "QiboTN cannot support initial state.")
state, rank = eval.expectation_pauli_tn_MPI(
circuit, self.dtype, 32)
state, rank = eval.expectation_pauli_tn_MPI(circuit, self.dtype, 32)
if rank > 0:
state = np.array(0)
if (
MPI_enabled == False
and MPS_enabled == False
and NCCL_enabled == True
and expectation_enabled == True
elif (
self.MPI_enabled == False
and self.MPS_enabled == False
and self.NCCL_enabled == True
and self.expectation_enabled == True
):
if initial_state is not None:
raise_error(NotImplementedError,
"QiboTN cannot support initial state.")
raise_error(NotImplementedError, "QiboTN cannot support initial state.")
state, rank = eval.expectation_pauli_tn_nccl(
circuit, self.dtype, 32)
state, rank = eval.expectation_pauli_tn_nccl(circuit, self.dtype, 32)
if rank > 0:
state = np.array(0)
else:
raise_error(NotImplementedError, "Backend not supported.")
if return_array:
return state.flatten()
else:
circuit._final_state = CircuitResult(
self, circuit, state.flatten(), nshots)
circuit._final_state = CircuitResult(self, circuit, state.flatten(), nshots)
return circuit._final_state