Add Op UnitTest for norm

python/tests/ops/test_norm_op.py

@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2023 CINN Authors. All Rights Reserved.
+#
+# Licensed 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 unittest
+import numpy as np
+from op_test import OpTest, OpTestTool
+from op_test_helper import TestCaseHelper
+import paddle
+import cinn
+from cinn.frontend import *
+from cinn.common import *
+
+
+@OpTestTool.skip_if(not is_compiled_with_cuda(),
+                    "x86 test will be skipped due to timeout.")
+class TestNormOp(OpTest):
+    def setUp(self):
+        print(f"\nRunning {self.__class__.__name__}: {self.case}")
+        self.prepare_inputs()
+
+    def prepare_inputs(self):
+        self.x_np = self.random(
+            shape=self.case["x_shape"], dtype=self.case["x_dtype"])
+
+    def build_paddle_program(self, target):
+        x = paddle.to_tensor(self.x_np, stop_gradient=True)
+        out = paddle.linalg.norm(
+            x,
+            p=self.case["p"],
+            axis=self.case["axis"],
+            keepdim=self.case["keepdim"])
+
+        self.paddle_outputs = [out]
+
+    def build_cinn_program(self, target):
+        builder = NetBuilder("norm")
+        x = builder.create_input(
+            self.nptype2cinntype(self.case["x_dtype"]), self.case["x_shape"],
+            "x")
+        out = builder.norm(
+            x, axis=self.case["axis"], epsilon=self.case["epsilon"])
+
+        prog = builder.build()
+        res = self.get_cinn_output(prog, target, [x], [self.x_np], [out])
+        self.cinn_outputs = [res[0]]
+
+    def test_check_results(self):
+        self.check_outputs_and_grads()
+
+
+class TestNormOpCase(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestNormOpCase"
+        self.cls = TestNormOp
+        self.inputs = [
+            {
+                "x_shape": [1],
+                "p": "fro",
+                "axis": -1,
+                "keepdim": False,
+                "epsilon": 1.0e-10,
+            },
+            {
+                "x_shape": [32],
+                "p": "inf",
+                "axis": None,
+                "keepdim": False,
+                "epsilon": 1.0e-10,
+            },
+            {
+                "x_shape": [1024],
+                "p": "-inf",
+                "axis": -1,
+                "keepdim": True,
+                "epsilon": 1.0e-10,
+            },
+            {
+                "x_shape": [32, 64],
+                "p": "0",
+                "axis": None,
+                "keepdim": False,
+                "epsilon": 1.0e-10,
+            },
+            {
+                "x_shape": [2, 3, 4],
+                "p": "1",
+                "axis": -1,
+                "keepdim": True,
+                "epsilon": 1.0e-10,
+            },
+            {
+                "x_shape": [16, 8, 4, 2],
+                "p": "2",
+                "axis": -1,
+                "keepdim": False,
+                "epsilon": 1.0e-10,
+            },
+            {
+                "x_shape": [16, 8, 4, 2, 1],
+                "p": "fro",
+                "axis": None,
+                "keepdim": True,
+                "epsilon": 1.0e-10,
+            },
+        ]
+
+        self.dtypes = [
+            {
+                "x_dtype": "float32",
+            },
+            {
+                "x_dtype": "float64",
+            },
+        ]
+
+        self.attrs = []
+
+
+if __name__ == "__main__":
+    TestNormOpCase().run()