add op unitest for logical_or (#1397)

python/tests/ops/test_logical_or_op.py

@@ -0,0 +1,191 @@
+# 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 TestLogicalOrOp(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"],
+            low=-10,
+            high=100)
+        self.y_np = self.random(
+            shape=self.case["y_shape"],
+            dtype=self.case["y_dtype"],
+            low=-10,
+            high=100)
+
+    def build_paddle_program(self, target):
+        x = paddle.to_tensor(self.x_np, stop_gradient=False)
+        y = paddle.to_tensor(self.y_np, stop_gradient=False)
+
+        def get_unsqueeze_axis(x_rank, y_rank, axis):
+            self.assertTrue(
+                x_rank >= y_rank,
+                "The rank of x should be greater or equal to that of y.")
+            axis = axis if axis >= 0 else x_rank - y_rank
+            unsqueeze_axis = np.arange(0, axis).tolist() + np.arange(
+                axis + y_rank, x_rank).tolist()
+            return unsqueeze_axis
+
+        unsqueeze_axis = get_unsqueeze_axis(
+            len(x.shape), len(y.shape), self.case["axis"])
+        y_t = paddle.unsqueeze(
+            y, axis=unsqueeze_axis) if len(unsqueeze_axis) > 0 else y
+        out = paddle.logical_or(x, y_t)
+
+        self.paddle_outputs = [out]
+
+    def build_cinn_program(self, target):
+        builder = NetBuilder("logical_and")
+        x = builder.create_input(
+            self.nptype2cinntype(self.case["x_dtype"]), self.case["x_shape"],
+            "x")
+        y = builder.create_input(
+            self.nptype2cinntype(self.case["y_dtype"]), self.case["y_shape"],
+            "y")
+        out = builder.logical_or(x, y, axis=self.case["axis"])
+
+        prog = builder.build()
+        res = self.get_cinn_output(prog, target, [x, y],
+                                   [self.x_np, self.y_np], [out])
+
+        self.cinn_outputs = res
+
+    def test_check_results(self):
+        max_relative_error = self.case[
+            "max_relative_error"] if "max_relative_error" in self.case else 1e-5
+        self.check_outputs_and_grads(max_relative_error=max_relative_error)
+
+
+class TestLogicalOrCase(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestLogicalOrCase"
+        self.cls = TestLogicalOrOp
+        self.inputs = [{
+            "x_shape": [1],
+            "y_shape": [1]
+        }, {
+            "x_shape": [1024],
+            "y_shape": [1024]
+        }, {
+            "x_shape": [512, 256],
+            "y_shape": [512, 256]
+        }, {
+            "x_shape": [128, 64, 32],
+            "y_shape": [128, 64, 32]
+        }, {
+            "x_shape": [128, 2048, 32],
+            "y_shape": [128, 2048, 32]
+        }, {
+            "x_shape": [16, 8, 4, 2],
+            "y_shape": [16, 8, 4, 2]
+        }, {
+            "x_shape": [1, 1, 1, 1],
+            "y_shape": [1, 1, 1, 1]
+        }, {
+            "x_shape": [16, 8, 4, 2, 1],
+            "y_shape": [16, 8, 4, 2, 1]
+        }]
+        self.dtypes = [{
+            "x_dtype": "bool",
+            "y_dtype": "bool"
+        }, {
+            "x_dtype": "int8",
+            "y_dtype": "int8"
+        }, {
+            "x_dtype": "int16",
+            "y_dtype": "int16"
+        }, {
+            "x_dtype": "int32",
+            "y_dtype": "int32"
+        }, {
+            "x_dtype": "int64",
+            "y_dtype": "int64"
+        }, {
+            "x_dtype": "float32",
+            "y_dtype": "float32"
+        }, {
+            "x_dtype": "float64",
+            "y_dtype": "float64"
+        }]
+        self.attrs = [{"axis": -1}]
+
+
+class TestLogicalOrCaseWithBroadcast(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestLogicalOrCaseWithBroadcast"
+        self.cls = TestLogicalOrOp
+        self.inputs = [{
+            "x_shape": [1],
+            "y_shape": [1]
+        }, {
+            "x_shape": [1024],
+            "y_shape": [1]
+        }, {
+            "x_shape": [512, 256],
+            "y_shape": [512, 1]
+        }, {
+            "x_shape": [128, 64, 32],
+            "y_shape": [128, 64, 1]
+        }, {
+            "x_shape": [16, 1, 1, 2],
+            "y_shape": [16, 8, 4, 2]
+        }, {
+            "x_shape": [16, 1, 1, 2, 1],
+            "y_shape": [16, 8, 4, 2, 1]
+        }]
+        self.dtypes = [{
+            "x_dtype": "bool",
+            "y_dtype": "bool"
+        }, {
+            "x_dtype": "int8",
+            "y_dtype": "int8"
+        }, {
+            "x_dtype": "int16",
+            "y_dtype": "int16"
+        }, {
+            "x_dtype": "int32",
+            "y_dtype": "int32"
+        }, {
+            "x_dtype": "int64",
+            "y_dtype": "int64"
+        }, {
+            "x_dtype": "float32",
+            "y_dtype": "float32"
+        }, {
+            "x_dtype": "float64",
+            "y_dtype": "float64"
+        }]
+        self.attrs = [{"axis": -1}]
+
+
+if __name__ == "__main__":
+    TestLogicalOrCase().run()
+    TestLogicalOrCaseWithBroadcast().run()