MNT: sparse import refactor (#695)

* MNT: `sparse` import refactor

* Add missing imports

* Refactor sparse/__init__.py

* Disable Finch backend in asv

Author: mtsokol

.github/workflows/ci.yml

@@ -53,7 +53,8 @@ jobs:
           pip install ${{ matrix.pip_opts }} numpy numba
       - name: Run tests
         run: |
-          pytest --pyargs sparse
+          SPARSE_BACKEND=Numba pytest --pyargs sparse
+          SPARSE_BACKEND=Finch pytest --pyargs sparse/tests
       - uses: codecov/codecov-action@v4
         if: always()
       - name: Publish Test Results

asv.conf.json

@@ -4,40 +4,17 @@
   ],
   "build_command": [],
   "environment_type": "virtualenv",
-  "exclude": [
-    {
-      "env": {
-        "SPARSE_BACKEND": "Finch"
-      },
-      "req": {
-        "finch-tensor": null
-      }
-    },
-    {
-      "env": {
-        "SPARSE_BACKEND": "Numba"
-      },
-      "req": {
-        "finch-tensor": ""
-      }
-    }
-  ],
   "install_command": [
     "pip install --no-deps ."
   ],
   "matrix": {
     "env": {
       "SPARSE_BACKEND": [
-        "Finch",
         "Numba"
       ]
     },
     "env_nobuild": {},
     "req": {
-      "finch-tensor": [
-        "",
-        null
-      ],
       "numba": [
         ""
       ],

benchmarks/benchmark_backends.py

@@ -2,13 +2,9 @@
 
 import numpy as np
 
-from .utils import skip_if_finch
-
 
 class MatrixMultiplySuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.x = sparse.random((100, 100), density=0.01, random_state=rng)
         self.y = sparse.random((100, 100), density=0.01, random_state=rng)
@@ -21,8 +17,6 @@ def time_matmul(self):
 
 class ElemwiseSuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.x = sparse.random((100, 100, 100), density=0.01, random_state=rng)
         self.y = sparse.random((100, 100, 100), density=0.01, random_state=rng)
@@ -39,8 +33,6 @@ def time_mul(self):
 
 class ElemwiseBroadcastingSuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.x = sparse.random((100, 1, 100), density=0.01, random_state=rng)
         self.y = sparse.random((100, 100), density=0.01, random_state=rng)
@@ -57,8 +49,6 @@ def time_mul(self):
 
 class IndexingSuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.index = rng.integers(0, 100, 50)
         self.x = sparse.random((100, 100, 100), density=0.01, random_state=rng)

benchmarks/benchmark_coo.py

@@ -2,13 +2,9 @@
 
 import numpy as np
 
-from .utils import skip_if_finch
-
 
 class MatrixMultiplySuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.x = sparse.random((100, 100), density=0.01, format="gcxs", random_state=rng)
         self.y = sparse.random((100, 100), density=0.01, format="gcxs", random_state=rng)
@@ -21,8 +17,6 @@ def time_matmul(self):
 
 class ElemwiseSuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.x = sparse.random((100, 100, 100), density=0.01, format="gcxs", random_state=rng)
         self.y = sparse.random((100, 100, 100), density=0.01, format="gcxs", random_state=rng)
@@ -38,8 +32,6 @@ def time_mul(self):
 
 class ElemwiseBroadcastingSuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.x = sparse.random((100, 1, 100), density=0.01, format="gcxs", random_state=rng)
         self.y = sparse.random((100, 100), density=0.01, format="gcxs", random_state=rng)
@@ -53,8 +45,6 @@ def time_mul(self):
 
 class IndexingSuite:
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.index = rng.integers(0, 100, 50)
         self.x = sparse.random((100, 100, 100), density=0.01, format="gcxs", random_state=rng)
@@ -84,8 +74,6 @@ class DenseMultiplySuite:
     param_names = ["compressed axis", "n_vectors"]
 
     def setup(self, compressed_axis, n_vecs):
-        skip_if_finch()
-
         rng = np.random.default_rng(1337)
         n = 10000
         x = sparse.random((n, n), density=0.001, format="gcxs", random_state=rng).change_compressed_axes(

benchmarks/benchmark_gcxs.py

@@ -2,15 +2,11 @@
 
 import numpy as np
 
-from .utils import skip_if_finch
-
 
 class Matmul_Sparse:
     params = (["coo", "gcxs"], [0, 1, None])
 
     def setup(self, p, dens_arg):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.x = sparse.random((100, 100), density=0.01, format=p, random_state=rng)
         self.y = sparse.random((100, 100), density=0.01, format=p, random_state=rng)

benchmarks/benchmark_matmul.py

@@ -2,8 +2,6 @@
 
 import numpy as np
 
-from .utils import skip_if_finch
-
 
 class TensordotSuiteDenseSparse:
     """
@@ -12,8 +10,6 @@ class TensordotSuiteDenseSparse:
     """
 
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.n = rng.random((100, 100))
         self.s = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
@@ -32,8 +28,6 @@ class TensordotSuiteSparseSparse:
     """
 
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.s1 = sparse.random((100, 100), density=0.01, random_state=rng)
         self.s2 = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
@@ -52,8 +46,6 @@ class TensordotSuiteSparseDense:
     """
 
     def setup(self):
-        skip_if_finch()
-
         rng = np.random.default_rng(0)
         self.s = sparse.random((100, 100, 100, 100), density=0.01, random_state=rng)
         self.n = rng.random((100, 100))