Make rank class attribute
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@@ -22,6 +22,7 @@ class CuTensorNet(QibotnBackend, NumpyBackend): # pragma: no cover
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self.configure_tn_simulation(runcard)
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def configure_tn_simulation(self, runcard):
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self.rank = None
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if runcard is not None:
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self.MPI_enabled = runcard.get("MPI_enabled", False)
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self.NCCL_enabled = runcard.get("NCCL_enabled", False)
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@@ -106,8 +107,8 @@ class CuTensorNet(QibotnBackend, NumpyBackend): # pragma: no cover
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and self.NCCL_enabled == False
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and self.expectation_enabled == False
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):
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state, rank = eval.dense_vector_tn_MPI(circuit, self.dtype, 32)
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if rank > 0:
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state, self.rank = eval.dense_vector_tn_MPI(circuit, self.dtype, 32)
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if self.rank > 0:
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state = np.array(0)
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elif (
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self.MPI_enabled == False
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@@ -115,8 +116,8 @@ class CuTensorNet(QibotnBackend, NumpyBackend): # pragma: no cover
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and self.NCCL_enabled == True
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and self.expectation_enabled == False
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):
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state, rank = eval.dense_vector_tn_nccl(circuit, self.dtype, 32)
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if rank > 0:
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state, self.rank = eval.dense_vector_tn_nccl(circuit, self.dtype, 32)
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if self.rank > 0:
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state = np.array(0)
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elif (
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self.MPI_enabled == False
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@@ -131,10 +132,10 @@ class CuTensorNet(QibotnBackend, NumpyBackend): # pragma: no cover
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and self.NCCL_enabled == False
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and self.expectation_enabled == True
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):
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state, rank = eval.expectation_tn_MPI(
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state, self.rank = eval.expectation_tn_MPI(
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circuit, self.dtype, self.observable, 32
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)
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if rank > 0:
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if self.rank > 0:
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state = np.array(0)
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elif (
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self.MPI_enabled == False
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@@ -142,10 +143,10 @@ class CuTensorNet(QibotnBackend, NumpyBackend): # pragma: no cover
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and self.NCCL_enabled == True
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and self.expectation_enabled == True
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):
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state, rank = eval.expectation_tn_nccl(
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state, self.rank = eval.expectation_tn_nccl(
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circuit, self.dtype, self.observable, 32
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)
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if rank > 0:
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if self.rank > 0:
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state = np.array(0)
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else:
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raise_error(NotImplementedError, "Compute type not supported.")
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