CuPy running out of memory
I have been testing the CuPy library and done a simple matrix multiplication using einsum:
C = cp.einsum('pqrs,rs->pq', A, B)
Dimensions of A and B are, (41, 41, 41, 41) (41, 41), receptively. I also checked their sizes, which are 22606088 bytes, 13448 bytes.
While running the code, I am getting the following error message:
OutOfMemoryError: out of memory to allocate 38000834048 bytes (total 38023468032 bytes)
It indicates that I am running out of memory. Is there any option to sent data partially to the device and perform operations in terms of batches?
python chainer cupy
edited Nov 16 at 5:21
talonmies
59.1k17128196
asked Nov 16 at 2:27
EveSz
162
add a comment |
I have been testing the CuPy library and done a simple matrix multiplication using einsum:
C = cp.einsum('pqrs,rs->pq', A, B)
Dimensions of A and B are, (41, 41, 41, 41) (41, 41), receptively. I also checked their sizes, which are 22606088 bytes, 13448 bytes.
While running the code, I am getting the following error message:
OutOfMemoryError: out of memory to allocate 38000834048 bytes (total 38023468032 bytes)
It indicates that I am running out of memory. Is there any option to sent data partially to the device and perform operations in terms of batches?
python chainer cupy
edited Nov 16 at 5:21
talonmies
59.1k17128196
asked Nov 16 at 2:27
EveSz
162
2
What CuPy version are you using? Could you add the output ofcupy.show_config()?
– Kenichi Maehashi
Nov 16 at 3:39
add a comment |
I have been testing the CuPy library and done a simple matrix multiplication using einsum:
C = cp.einsum('pqrs,rs->pq', A, B)
Dimensions of A and B are, (41, 41, 41, 41) (41, 41), receptively. I also checked their sizes, which are 22606088 bytes, 13448 bytes.
While running the code, I am getting the following error message:
OutOfMemoryError: out of memory to allocate 38000834048 bytes (total 38023468032 bytes)
It indicates that I am running out of memory. Is there any option to sent data partially to the device and perform operations in terms of batches?
python chainer cupy
edited Nov 16 at 5:21
talonmies
59.1k17128196
asked Nov 16 at 2:27
EveSz
162
I have been testing the CuPy library and done a simple matrix multiplication using einsum:
C = cp.einsum('pqrs,rs->pq', A, B)
Dimensions of A and B are, (41, 41, 41, 41) (41, 41), receptively. I also checked their sizes, which are 22606088 bytes, 13448 bytes.
While running the code, I am getting the following error message:
OutOfMemoryError: out of memory to allocate 38000834048 bytes (total 38023468032 bytes)
It indicates that I am running out of memory. Is there any option to sent data partially to the device and perform operations in terms of batches?
python chainer cupy
python chainer cupy
edited Nov 16 at 5:21
talonmies
59.1k17128196
asked Nov 16 at 2:27
EveSz
162
edited Nov 16 at 5:21
talonmies
59.1k17128196
asked Nov 16 at 2:27
EveSz
162
edited Nov 16 at 5:21
talonmies
59.1k17128196
edited Nov 16 at 5:21
talonmies
59.1k17128196
edited Nov 16 at 5:21
talonmies
59.1k17128196
59.1k17128196
asked Nov 16 at 2:27
EveSz
162
asked Nov 16 at 2:27
EveSz
162
asked Nov 16 at 2:27
EveSz
162
162
2
What CuPy version are you using? Could you add the output ofcupy.show_config()?
– Kenichi Maehashi
Nov 16 at 3:39
add a comment |
2
What CuPy version are you using? Could you add the output ofcupy.show_config()?
– Kenichi Maehashi
Nov 16 at 3:39
2
2
What CuPy version are you using? Could you add the output of
cupy.show_config()?– Kenichi Maehashi
Nov 16 at 3:39
What CuPy version are you using? Could you add the output of
cupy.show_config()?– Kenichi Maehashi
Nov 16 at 3:39
add a comment |
1 Answer
1
active
oldest
votes
I think there is no option to send data partially for one-array.
And I faced same issue before, this may be caused because the cupy einsum efficiency is not optimized yet.
https://github.com/cupy/cupy/issues/19#issuecomment-322972682
If you can try replacing your einsum function by using transpose, reshape and matmul etc, please try those.
I guess
C = cp.einsum('pqrs,rs->pq', A, B)
is equivalent to
p, q, r, s = A.shape
A = cp.reshape(A, (p, q, r*s))
B = cp.reshape(B, (1, 1, r*s))
C = cp.sum(A * B, axis=2)
answered Nov 16 at 2:58
corochann
1,0701618
You are right, this way works. It looks the CuPy einsum is not optimized. I also noticed that this library does not work if the available memory is excited, what is a huge drawback. Well, we know that GPU cards do not provide to much memory. Have you tried to go around it?
– EveSz
Nov 17 at 16:03
1
I thinkeinsumimplementation is updated, I don't know which cupy version you are using but latest version may work more efficiently.
– corochann
Nov 18 at 13:21
add a comment |
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1 Answer
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votes
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active
oldest
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oldest
votes
I think there is no option to send data partially for one-array.
And I faced same issue before, this may be caused because the cupy einsum efficiency is not optimized yet.
https://github.com/cupy/cupy/issues/19#issuecomment-322972682
If you can try replacing your einsum function by using transpose, reshape and matmul etc, please try those.
I guess
C = cp.einsum('pqrs,rs->pq', A, B)
is equivalent to
p, q, r, s = A.shape
A = cp.reshape(A, (p, q, r*s))
B = cp.reshape(B, (1, 1, r*s))
C = cp.sum(A * B, axis=2)
answered Nov 16 at 2:58
corochann
1,0701618
You are right, this way works. It looks the CuPy einsum is not optimized. I also noticed that this library does not work if the available memory is excited, what is a huge drawback. Well, we know that GPU cards do not provide to much memory. Have you tried to go around it?
– EveSz
Nov 17 at 16:03
1
I thinkeinsumimplementation is updated, I don't know which cupy version you are using but latest version may work more efficiently.
– corochann
Nov 18 at 13:21
add a comment |
I think there is no option to send data partially for one-array.
And I faced same issue before, this may be caused because the cupy einsum efficiency is not optimized yet.
https://github.com/cupy/cupy/issues/19#issuecomment-322972682
If you can try replacing your einsum function by using transpose, reshape and matmul etc, please try those.
I guess
C = cp.einsum('pqrs,rs->pq', A, B)
is equivalent to
p, q, r, s = A.shape
A = cp.reshape(A, (p, q, r*s))
B = cp.reshape(B, (1, 1, r*s))
C = cp.sum(A * B, axis=2)
answered Nov 16 at 2:58
corochann
1,0701618
You are right, this way works. It looks the CuPy einsum is not optimized. I also noticed that this library does not work if the available memory is excited, what is a huge drawback. Well, we know that GPU cards do not provide to much memory. Have you tried to go around it?
– EveSz
Nov 17 at 16:03
1
I thinkeinsumimplementation is updated, I don't know which cupy version you are using but latest version may work more efficiently.
– corochann
Nov 18 at 13:21
add a comment |
I think there is no option to send data partially for one-array.
And I faced same issue before, this may be caused because the cupy einsum efficiency is not optimized yet.
https://github.com/cupy/cupy/issues/19#issuecomment-322972682
If you can try replacing your einsum function by using transpose, reshape and matmul etc, please try those.
I guess
C = cp.einsum('pqrs,rs->pq', A, B)
is equivalent to
p, q, r, s = A.shape
A = cp.reshape(A, (p, q, r*s))
B = cp.reshape(B, (1, 1, r*s))
C = cp.sum(A * B, axis=2)
answered Nov 16 at 2:58
corochann
1,0701618
I think there is no option to send data partially for one-array.
And I faced same issue before, this may be caused because the cupy einsum efficiency is not optimized yet.
https://github.com/cupy/cupy/issues/19#issuecomment-322972682
If you can try replacing your einsum function by using transpose, reshape and matmul etc, please try those.
I guess
C = cp.einsum('pqrs,rs->pq', A, B)
is equivalent to
p, q, r, s = A.shape
A = cp.reshape(A, (p, q, r*s))
B = cp.reshape(B, (1, 1, r*s))
C = cp.sum(A * B, axis=2)
answered Nov 16 at 2:58
corochann
1,0701618
answered Nov 16 at 2:58
corochann
1,0701618
answered Nov 16 at 2:58
corochann
1,0701618
answered Nov 16 at 2:58
corochann
1,0701618
1,0701618
You are right, this way works. It looks the CuPy einsum is not optimized. I also noticed that this library does not work if the available memory is excited, what is a huge drawback. Well, we know that GPU cards do not provide to much memory. Have you tried to go around it?
– EveSz
Nov 17 at 16:03
1
I thinkeinsumimplementation is updated, I don't know which cupy version you are using but latest version may work more efficiently.
– corochann
Nov 18 at 13:21
add a comment |
You are right, this way works. It looks the CuPy einsum is not optimized. I also noticed that this library does not work if the available memory is excited, what is a huge drawback. Well, we know that GPU cards do not provide to much memory. Have you tried to go around it?
– EveSz
Nov 17 at 16:03
1
I thinkeinsumimplementation is updated, I don't know which cupy version you are using but latest version may work more efficiently.
– corochann
Nov 18 at 13:21
You are right, this way works. It looks the CuPy einsum is not optimized. I also noticed that this library does not work if the available memory is excited, what is a huge drawback. Well, we know that GPU cards do not provide to much memory. Have you tried to go around it?
– EveSz
Nov 17 at 16:03
You are right, this way works. It looks the CuPy einsum is not optimized. I also noticed that this library does not work if the available memory is excited, what is a huge drawback. Well, we know that GPU cards do not provide to much memory. Have you tried to go around it?
– EveSz
Nov 17 at 16:03
1
1
I think
einsum implementation is updated, I don't know which cupy version you are using but latest version may work more efficiently.– corochann
Nov 18 at 13:21
I think
einsum implementation is updated, I don't know which cupy version you are using but latest version may work more efficiently.– corochann
Nov 18 at 13:21
add a comment |
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2
What CuPy version are you using? Could you add the output of
cupy.show_config()?– Kenichi Maehashi
Nov 16 at 3:39