def _transpose_sparse(spenv, *argspecs, permutation):
permutation = tuple(permutation)
args = argspecs_to_arrays(spenv, argspecs)
shape = args[0].shape
data, indices = sparse.bcoo_transpose(args[0].data,
args[0].indices,
permutation=permutation,
shape=shape)
out_shape = tuple(shape[i] for i in permutation)
n_batch = args[0].indices.ndim - 2
n_sparse = args[0].indices.shape[-2]
batch_dims_unchanged = (permutation[:n_batch] == tuple(range(n_batch)))
dense_dims_unchanged = (permutation[n_batch + n_sparse:] == tuple(
range(n_batch + n_sparse, len(shape))))
sparse_dims_unchanged = (permutation[n_batch:n_batch + n_sparse] == tuple(
range(n_batch, n_batch + n_sparse)))
# Data is unchanged if batch & dense dims are not permuted
if batch_dims_unchanged and dense_dims_unchanged:
data_ref = argspecs[0].data_ref
else:
data_ref = spenv.push(data)
# Indices unchanged if batch & sparse dims are not permuted
if batch_dims_unchanged and sparse_dims_unchanged:
indices_ref = argspecs[0].indices_ref
else:
indices_ref = spenv.push(indices)
argspec = ArgSpec(out_shape, data_ref, indices_ref)
return (argspec, )
def _transpose_sparse(spenv, *spvalues, permutation):
permutation = tuple(permutation)
args = spvalues_to_arrays(spenv, spvalues)
shape = args[0].shape
mat = sparse.BCOO((args[0].data, args[0].indices), shape=shape)
mat_transposed = sparse.bcoo_transpose(mat, permutation=permutation)
out_shape = tuple(shape[i] for i in permutation)
n_batch = args[0].indices.ndim - 2
n_sparse = args[0].indices.shape[-1]
batch_dims_unchanged = (permutation[:n_batch] == tuple(range(n_batch)))
dense_dims_unchanged = (permutation[n_batch + n_sparse:] == tuple(range(n_batch + n_sparse, len(shape))))
sparse_dims_unchanged = (permutation[n_batch:n_batch + n_sparse] == tuple(range(n_batch, n_batch + n_sparse)))
# Data is unchanged if batch & dense dims are not permuted
kwds = {}
if batch_dims_unchanged and dense_dims_unchanged:
kwds['data_ref'] = spvalues[0].data_ref
else:
kwds['data'] = mat_transposed.data
# Indices unchanged if batch & sparse dims are not permuted
if batch_dims_unchanged and sparse_dims_unchanged:
kwds['indices_ref'] = spvalues[0].indices_ref
else:
kwds['indices'] = mat_transposed.indices
spvalue = spenv.sparse(out_shape, **kwds)
return (spvalue,)