chore: import upstream snapshot with attribution

tests/unit/runtime/sparse_tensor/test_csr.py

@@ -0,0 +1,55 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+import random
+from deepspeed.runtime.sparse_tensor import SparseTensor
+
+
+def test_csr_addition_self():
+    row_count = 10
+    random.seed(1234)
+
+    x = torch.ones(1, 5)
+    for i in range(row_count - 1):
+        if random.random() > 0.75:
+            x = torch.cat([x, torch.ones(1, 5)])
+        else:
+            x = torch.cat([x, torch.zeros(1, 5)])
+    dense_x = x.clone()
+    cx = SparseTensor(x)
+
+    assert torch.all(dense_x == cx.to_dense())
+
+    cx.add(cx)
+    assert torch.all(dense_x + dense_x == cx.to_dense())
+
+
+def test_csr_addition_different():
+    row_count = 10
+    random.seed(1234)
+
+    x = torch.ones(1, 5)
+    for i in range(row_count - 1):
+        if random.random() > 0.75:
+            x = torch.cat([x, torch.ones(1, 5)])
+        else:
+            x = torch.cat([x, torch.zeros(1, 5)])
+    dense_x = x.clone()
+    cx = SparseTensor(x)
+
+    y = torch.ones(1, 5)
+    for i in range(row_count - 1):
+        if random.random() > 0.75:
+            y = torch.cat([y, torch.ones(1, 5)])
+        else:
+            y = torch.cat([y, torch.zeros(1, 5)])
+    dense_y = y.clone()
+    cy = SparseTensor(y)
+
+    dense_sum = dense_x + dense_y
+    cx.add(cy)
+
+    assert torch.all(dense_sum == cx.to_dense())