Closed yCobanoglu closed 11 months ago
asistradition
commented
12 months ago This is an error that's thrown when scipy.sparse.issparse() returns True on your input, but is_csr, is_csc, and is_bsr all return False. It's thrown before anything is passed into MKL structs, so it's unlikely to be an issue with MKL.
This is a pretty simple logical check and I don't see how it could be failing - I would need a reproducible example to check.
yCobanoglu
commented
12 months ago ok i can check but if i pass dot_product_mkl(scipy.sparse.csr_array(a), scipy.spare.csr_array(b)) this error should not happen right ?
I will give an error log tomorrow with that setup. I will be hard to reproduce but i had the issues with the Compute Optimized VMs from Google Cloud.
yCobanoglu
commented
11 months ago @asistradition Here the code throws an error although i check first if the matrices are sparse and then even convert them to sparse right before passing them to the function ( you can see that from the error i convert both matrix to sparse right before passing the to the sparse dot product):
return dot_product_mkl(scipy.sparse.csr_array(cov), scipy.sparse.csr_array(cov_vec)).toarray()
The code snipped that throws the error:
def batch_elem_mul_mkl(cov):
cov = scipy.sparse.csr_array(cov)
def f(cov_adj):
cov_vec = cov_adj.reshape((cov.shape[1], -1), order="F")
cov_vec = scipy.sparse.csr_array(cov_vec)
# does sparse dot product parallel but sometimes there is a bug on certain machines
# ValueError: Input matrices to dot_product_mkl must be CSR, CSC, or BSR; COO is not supported although all arrays are csr
if not scipy.sparse.issparse(cov) or not scipy.sparse.issparse(cov_vec):
raise ValueError("My Error: Input matrices to dot_product_mkl must sparse")
return dot_product_mkl(scipy.sparse.csr_array(cov), scipy.sparse.csr_array(cov_vec)).toarray()
return fThe error
Traceback (most recent call last):
File "/home/yunus.ebabylon/graph-neural-networks/gnn/infinite_width/run.py", line 263, in <module>
gat_gp, gat_ntk_, time1 = with_time(lambda: gat_ntk(adj, x, layer, SIGMA_B, SIGMA_W, NONLINEAR))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/yunus.ebabylon/graph-neural-networks/gnn/infinite_width/run.py", line 161, in with_time
res = f()
^^^
File "/home/yunus.ebabylon/graph-neural-networks/gnn/infinite_width/run.py", line 263, in <lambda>
gat_gp, gat_ntk_, time1 = with_time(lambda: gat_ntk(adj, x, layer, SIGMA_B, SIGMA_W, NONLINEAR))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/yunus.ebabylon/graph-neural-networks/gnn/infinite_width/gat/gat_ntk.py", line 32, in gat_ntk
return forward(kernel_init, kernel_init, 1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/yunus.ebabylon/graph-neural-networks/gnn/infinite_width/gat/gat_ntk.py", line 24, in forward
nngp = batch_mul(sigma, cov_att)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/yunus.ebabylon/graph-neural-networks/gnn/infinite_width/gat/utils.py", line 58, in batch_mul
results = np.vstack(list(results))
^^^^^^^^^^^^^
File "/home/yunus.ebabylon/graph-neural-networks/gnn/infinite_width/gat/utils.py", line 22, in f
return dot_product_mkl(scipy.sparse.csr_array(cov), scipy.sparse.csr_array(cov_vec)).toarray()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/yunus.ebabylon/graph-neural-networks/.venv/lib/python3.11/site-packages/sparse_dot_mkl/sparse_dot.py", line 58, in dot_product_mkl
return _sds(matrix_a, matrix_b, cast=cast, reorder_output=reorder_output, dense=dense)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/yunus.ebabylon/graph-neural-networks/.venv/lib/python3.11/site-packages/sparse_dot_mkl/_sparse_sparse.py", line 114, in _sparse_dot_sparse
raise ValueError("Input matrices to dot_product_mkl must be CSR, CSC, or BSR; COO is not supported")
ValueError: Input matrices to dot_product_mkl must be CSR, CSC, or BSR; COO is not supportedAnd as i mentioned i can only reproduce the errors on machines on the cloud not locally.
asistradition
commented
11 months ago Looks like scipy is refactoring *_matrix into *_array objects and that is probably the reason for the error. If you can use csr_matrix for now that should work and I will find some time to make the _array objects work soon.
It's probably also very dependent on the scipy version, someone is developing that module now.
yCobanoglu
commented
11 months ago Yeah you were right using scipy.csr_matrix fixed it. Thanks !
asistradition
commented
11 months ago v0.9.0 should support *_array in place of *_matrix
Hello, thanks for the great library, first of all. I get this error just on certain machines. The same code words locally and on an Machine 1) and 2) but not on Machine 3). This has to be related to the Architecture or Intel MKL not sure what to make of it.
The error has nothing to do with the fact that the matrices are not sparse.
gram_matrix_mklworks fine.