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[WIP] CSR/ CSC Elemwise #465

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[WIP] CSR/ CSC Elemwise #465

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9db444f
Basic type promotion and computation for CSC/ CSR broadcasting
ivirshup Apr 21, 2021
e312914
Fixup test
ivirshup Apr 21, 2021
91671c9
Minor cleanup (might have broke something)
ivirshup Apr 21, 2021
7f2d2ed
temp print statement
ivirshup Apr 22, 2021
5ad790e
Fix return type determination
ivirshup Apr 22, 2021
34ce299
Fix fill_value argument handling for CSC and CSR, handle scipy -> spa…
ivirshup Apr 22, 2021
ccffedc
Unbreak elemwise ops with CSR/CSC and COO
ivirshup Apr 22, 2021
30004b9
Skip some tests with densification
ivirshup Apr 22, 2021
c9510af
Resize array
ivirshup Apr 24, 2021
c96cc1a
Use pruning strategy from scipy instead of resizing (it seems a bit f…
ivirshup Apr 24, 2021
9b7f22c
Merge branch 'master' into elemwise
ivirshup Jul 20, 2023
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Resize array
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@ivirshup
ivirshup committed Apr 24, 2021
commit c9510af87a4467eed3005d6fee257808b1cd0674
74 changes: 48 additions & 26 deletions sparse/_compressed/elemwise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,20 +40,27 @@ def op_union_indices(
# TODO: numpy is weird with bools here
out_dtype = np.array(op(a.data[0], b.data[0])).dtype
default_value = out_dtype.type(default_value)
return type(a)(
op_union_indices_csr_csr(
out_indptr = np.zeros_like(a.indptr)
out_indices = np.zeros(len(a.indices) + len(b.indices), dtype=np.promote_types(a.indices.dtype, b.indices.dtype))
out_data = np.zeros(len(out_indices), dtype=out_dtype)

nnz = op_union_indices_csr_csr(
op,
a.indptr,
a.indices,
a.data,
b.indptr,
b.indices,
b.data,
out_indptr,
out_indices,
out_data,
out_dtype=out_dtype,
default_value=default_value,
),
a.shape,
)
)
out_data.resize(nnz)
out_indices.resize(nnz)
return type(a)((out_data, out_indices, out_indptr), shape=a.shape)


@njit
Expand All @@ -65,12 +72,15 @@ def op_union_indices_csr_csr(
b_indptr: np.ndarray,
b_indices: np.ndarray,
b_data: np.ndarray,
out_indptr: np.ndarray,
out_indices: np.ndarray,
out_data: np.ndarray,
out_dtype,
default_value,
):
out_indptr = np.zeros_like(a_indptr)
out_indices = np.zeros(len(a_indices) + len(b_indices), dtype=a_indices.dtype)
out_data = np.zeros(len(out_indices), dtype=out_dtype)
# out_indptr = np.zeros_like(a_indptr)
# out_indices = np.zeros(len(a_indices) + len(b_indices), dtype=a_indices.dtype)
# out_data = np.zeros(len(out_indices), dtype=out_dtype)

out_idx = 0

Expand All @@ -85,38 +95,50 @@ def op_union_indices_csr_csr(
a_j = a_indices[a_idx]
b_j = b_indices[b_idx]
if a_j < b_j:
out_indices[out_idx] = a_j
out_data[out_idx] = op(a_data[a_idx], default_value)
val = op(a_data[a_idx], default_value)
if val != default_value:
out_indices[out_idx] = a_j
out_data[out_idx] = val
out_idx += 1
a_idx += 1
elif b_j < a_j:
out_indices[out_idx] = b_j
out_data[out_idx] = op(default_value, b_data[b_idx])
val = op(default_value, b_data[b_idx])
if val != default_value:
out_indices[out_idx] = b_j
out_data[out_idx] = val
out_idx += 1
b_idx += 1
else:
out_indices[out_idx] = a_j
out_data[out_idx] = op(a_data[a_idx], b_data[b_idx])
val = op(a_data[a_idx], b_data[b_idx])
if val != default_value:
out_indices[out_idx] = a_j
out_data[out_idx] = val
out_idx += 1
a_idx += 1
b_idx += 1
out_idx += 1

# Catch up the other set
while a_idx < a_end:
a_j = a_indices[a_idx]
out_indices[out_idx] = a_j
out_data[out_idx] = op(a_data[a_idx], default_value)
val = op(a_data[a_idx], default_value)
if val != default_value:
out_indices[out_idx] = a_indices[a_idx]
out_data[out_idx] = val
out_idx += 1
a_idx += 1
out_idx += 1

while b_idx < b_end:
b_j = b_indices[b_idx]
out_indices[out_idx] = b_j
out_data[out_idx] = op(default_value, b_data[b_idx])
val = op(default_value, b_data[b_idx])
if val != default_value:
out_indices[out_idx] = b_indices[b_idx]
out_data[out_idx] = val
out_idx += 1
b_idx += 1
out_idx += 1

out_indptr[i + 1] = out_idx

out_indices = out_indices[: out_idx]
out_data = out_data[: out_idx]
# This may need to change to be "resize" to allow memory reallocation
# resize is currently not implemented in numba
# out_indices = out_indices[: out_idx]
# out_data = out_data[: out_idx]

return out_data, out_indices, out_indptr
return out_idx
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