chore: import upstream snapshot with attribution

tests/python/relax/test_op_size.py

@@ -0,0 +1,63 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import numpy as np
+
+import tvm
+import tvm.testing
+from tvm import relax
+from tvm.script import relax as R
+
+
+def test_op_size():
+    @tvm.script.ir_module
+    class Module:
+        @R.function
+        def main(x: R.Tensor((2, 3), "float32")) -> R.Tensor((), "int64"):
+            return R.size(x)
+
+    x_np = np.random.rand(2, 3).astype("float32")
+    x = tvm.runtime.tensor(x_np)
+
+    target = tvm.target.Target("llvm")
+    ex = relax.build(Module, target)
+    vm = relax.VirtualMachine(ex, tvm.cpu())
+
+    res = vm["main"](x)
+    assert res.numpy() == 6
+
+
+def test_op_size_dynamic():
+    @tvm.script.ir_module
+    class Module:
+        @R.function
+        def main(x: R.Tensor(("m", "n"), "float32")) -> R.Tensor((), "int64"):
+            return R.size(x)
+
+    x_np = np.random.rand(4, 5).astype("float32")
+    x = tvm.runtime.tensor(x_np)
+
+    target = tvm.target.Target("llvm")
+    ex = relax.build(Module, target)
+    vm = relax.VirtualMachine(ex, tvm.cpu())
+
+    res = vm["main"](x)
+    assert res.numpy() == 20
+
+
+if __name__ == "__main__":
+    tvm.testing.main()