[Misc] Reorg python test (with backend) according to the guideline. (#5312)

* rename

* remove test transpose

* udpate

* rename

* cleanup

---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

tests/pytorch/sparse/test_sparse_matrix.py → tests/python/pytorch/sparse/test_sparse_matrix.py

@@ -480,3 +480,26 @@ def test_to_dtype(dtype):
     mat2 = getattr(mat, func_name[dtype])()
     assert mat2.shape == mat.shape
     assert torch.allclose(mat2.val, target_val)
+
+
+@pytest.mark.parametrize("dense_dim", [None, 2])
+@pytest.mark.parametrize("row", [[0, 0, 1, 2], (0, 1, 2, 4)])
+@pytest.mark.parametrize("col", [(0, 1, 2, 2), (1, 3, 3, 4)])
+@pytest.mark.parametrize("extra_shape", [(0, 1), (2, 1)])
+def test_sparse_matrix_transpose(dense_dim, row, col, extra_shape):
+    mat_shape = (max(row) + 1 + extra_shape[0], max(col) + 1 + extra_shape[1])
+    val_shape = (len(row),)
+    if dense_dim is not None:
+        val_shape += (dense_dim,)
+    ctx = F.ctx()
+    val = torch.randn(val_shape).to(ctx)
+    row = torch.tensor(row).to(ctx)
+    col = torch.tensor(col).to(ctx)
+    mat = from_coo(row, col, val, mat_shape).transpose()
+    mat_row, mat_col = mat.coo()
+    mat_val = mat.val
+
+    assert mat.shape == mat_shape[::-1]
+    assert torch.allclose(mat_val, val)
+    assert torch.allclose(mat_row, col)
+    assert torch.allclose(mat_col, row)

tests/pytorch/sparse/test_transpose.py

-import sys
-
-import backend as F
-import pytest
-import torch
-
-from dgl.sparse import diag, from_coo
-
-
-@pytest.mark.parametrize("val_shape", [(3,), (3, 2)])
-@pytest.mark.parametrize("mat_shape", [None, (3, 5), (5, 3)])
-def test_diag_matrix_transpose(val_shape, mat_shape):
-    ctx = F.ctx()
-    val = torch.randn(val_shape).to(ctx)
-    mat = diag(val, mat_shape).transpose()
-
-    assert torch.allclose(mat.val, val)
-    if mat_shape is None:
-        mat_shape = (val_shape[0], val_shape[0])
-    assert mat.shape == mat_shape[::-1]
-
-
-@pytest.mark.parametrize("dense_dim", [None, 2])
-@pytest.mark.parametrize("row", [[0, 0, 1, 2], (0, 1, 2, 4)])
-@pytest.mark.parametrize("col", [(0, 1, 2, 2), (1, 3, 3, 4)])
-@pytest.mark.parametrize("extra_shape", [(0, 1), (2, 1)])
-def test_sparse_matrix_transpose(dense_dim, row, col, extra_shape):
-    mat_shape = (max(row) + 1 + extra_shape[0], max(col) + 1 + extra_shape[1])
-    val_shape = (len(row),)
-    if dense_dim is not None:
-        val_shape += (dense_dim,)
-    ctx = F.ctx()
-    val = torch.randn(val_shape).to(ctx)
-    row = torch.tensor(row).to(ctx)
-    col = torch.tensor(col).to(ctx)
-    mat = from_coo(row, col, val, mat_shape).transpose()
-    mat_row, mat_col = mat.coo()
-    mat_val = mat.val
-
-    assert mat.shape == mat_shape[::-1]
-    assert torch.allclose(mat_val, val)
-    assert torch.allclose(mat_row, col)
-    assert torch.allclose(mat_col, row)

tests/scripts/task_unit_test.bat

@@ -15,7 +15,7 @@ SET DGL_LIBRARY_PATH=!CD!\build
 SET DGL_DOWNLOAD_DIR=!CD!
 
 python -m pip install pytest psutil pandas pyyaml pydantic rdflib torchmetrics || EXIT /B 1
-python -m pytest -v --junitxml=pytest_backend.xml --durations=100 tests\!DGLBACKEND! || EXIT /B 1
+python -m pytest -v --junitxml=pytest_backend.xml --durations=100 tests\python\!DGLBACKEND! || EXIT /B 1
 python -m pytest -v --junitxml=pytest_common.xml --durations=100 tests\python\common || EXIT /B 1
 ENDLOCAL
 EXIT /B

tests/scripts/task_unit_test.sh

@@ -39,4 +39,4 @@ then
 else
   python3 -m pytest -v --junitxml=pytest_common.xml --durations=100 tests/python/common || fail "common"
 fi
-python3 -m pytest -v --junitxml=pytest_backend.xml --durations=100 tests/$DGLBACKEND || fail "backend-specific"
+python3 -m pytest -v --junitxml=pytest_backend.xml --durations=100 tests/python/$DGLBACKEND || fail "backend-specific"