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Python support for GPU ops [WIP] #759

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Python support for GPU ops [WIP] #759

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bd2b612
broken version of ops
Sinestro38 Mar 29, 2023
d749b05
Add cuquantum_configure()
jaeyoo Mar 30, 2023
1831ec1
Merge branch 'master' into custatevec_support_v4
jaeyoo Mar 30, 2023
9ae4fd0
Add CUQUANTUM_ROOT detection for automatic setup in configure.sh
jaeyoo Mar 30, 2023
eca1f01
Merge pull request #4 from tensorflow/master
jaeyoo Mar 30, 2023
2e29308
Fix errors
jaeyoo Mar 30, 2023
60a2ee9
Merge branch 'master' into custatevec_support_v4
jaeyoo Mar 30, 2023
67b8e81
Split cuda/cuquantum and fix cuquantum_configure error
jaeyoo Mar 30, 2023
dcf189f
modify kernels and add python wrapper file
Sinestro38 Mar 30, 2023
3eae118
linting
Sinestro38 Mar 30, 2023
c5e0235
Add simulate cuquantum ops
jaeyoo Mar 30, 2023
41c1839
Fix lint and format
jaeyoo Mar 30, 2023
7189efc
Fix lint
jaeyoo Mar 30, 2023
8b2ce11
Merge pull request #5 from jaeyoo/custatevec_support_v5
jaeyoo Mar 30, 2023
615a949
merge master
Sinestro38 Mar 31, 2023
e9dfeb4
merge master ops BUILD file
Sinestro38 Mar 31, 2023
80861ae
cuquantum support for gpu ops, need to add CUDA support tho
Sinestro38 Apr 1, 2023
ae9cac5
python support for adj grad cuqantum op (needs to be merged WIP)
Sinestro38 Apr 1, 2023
d830a3b
cuquantum support for circuit executor ops
Sinestro38 Apr 1, 2023
c97fa3a
keras layers cuquantum support
Sinestro38 Apr 1, 2023
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python support for adj grad cuqantum op (needs to be merged WIP)
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@Sinestro38
Sinestro38 committed Apr 1, 2023
commit ae9cac57e3fd3456a706b9fe2d191fd1822d9b5e
14 changes: 9 additions & 5 deletions tensorflow_quantum/python/differentiators/adjoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
"""Compute gradients by combining function values linearly."""
import tensorflow as tf

from tensorflow_quantum.core.ops import tfq_adj_grad_op
from tensorflow_quantum.core.ops import tfq_adj_grad_op, tfq_adj_grad_op_cuquantum
from tensorflow_quantum.python.differentiators import differentiator


Expand Down Expand Up @@ -98,13 +98,17 @@ def get_gradient_circuits(self, programs, symbol_names, symbol_values):
@differentiator.catch_empty_inputs
@tf.function
def differentiate_analytic(self, programs, symbol_names, symbol_values,
pauli_sums, forward_pass_vals, grad):
return tfq_adj_grad_op.tfq_adj_grad(programs, symbol_names,
symbol_values, pauli_sums, grad)
pauli_sums, forward_pass_vals, grad, use_gpu=False):
if use_gpu:
return tfq_adj_grad_op_cuquantum.tfq_adj_grad(programs, symbol_names,
symbol_values, pauli_sums, grad)
else:
return tfq_adj_grad_op.tfq_adj_grad(programs, symbol_names,
symbol_values, pauli_sums, grad)

def differentiate_sampled(self, programs, symbol_names, symbol_values,
pauli_sums, num_samples, forward_pass_vals, grad):
raise NotImplementedError(
"Adjoint state methods are not supported in sample based settings."
" Please use analytic expectation calculation or a different "
"tfq.differentiator.")
"tfq.differentiator.")