chore: import upstream snapshot with attribution

tests/python/relax/test_frontend_nn_debug.py

@@ -0,0 +1,83 @@
+# 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.
+# pylint: disable=missing-docstring
+import torch
+
+import tvm
+import tvm.testing
+from tvm import tirx
+from tvm.relax.frontend import nn
+from tvm.relax.frontend.nn import op, spec
+from tvm.runtime import Tensor
+
+
+def test_debug_print():
+    class Layer(nn.Module):
+        def forward(self, x: nn.Tensor):  # pylint: disable=invalid-name
+            op.print_(x)
+            return x
+
+    model = Layer().jit(
+        spec={
+            "forward": {"x": spec.Tensor([10, 5], dtype="float32")},
+        },
+        debug=True,
+    )
+    x = torch.rand((10, 5), dtype=torch.float32)  # pylint: disable=invalid-name
+    y = model["forward"](x)  # pylint: disable=invalid-name
+    assert isinstance(y, torch.Tensor)
+
+
+def test_debug_func():
+    @tvm.register_global_func("testing.relax.frontend.nn.test_debug_func")
+    def _debug(  # pylint: disable=too-many-arguments
+        lineno: str,
+        tensor: Tensor,
+        const_int: int,
+        const_float: float,
+        const_str: str,
+        var_int: int,
+    ) -> None:
+        assert "test_frontend_nn_debug.py" in lineno
+        assert tensor.shape == (10, 5)
+        assert const_int == 1
+        assert const_float == 2.0
+        assert const_str == "test"
+        assert var_int == 8
+
+    class Layer(nn.Module):
+        def forward(self, x: nn.Tensor, v: tirx.Var):  # pylint: disable=invalid-name
+            op.debug_func("testing.relax.frontend.nn.test_debug_func", x, 1, 2.0, "test", v)
+            return x
+
+    model = Layer().jit(
+        spec={
+            "forward": {
+                "x": spec.Tensor([10, 5], dtype="float32"),
+                "v": "int",
+            },
+        },
+        debug=True,
+    )
+    x = torch.rand((10, 5), dtype=torch.float32)  # pylint: disable=invalid-name
+    y = model["forward"](x, 8)  # pylint: disable=invalid-name
+    assert isinstance(y, torch.Tensor)
+
+
+if __name__ == "__main__":
+    test_debug_print()
+    test_debug_func()