Add unitest for is_inf/is_finite/is_nan (#1483)

* add unitest for is_inf/is_finite/is_nan

* fix bugs

* fix bugs

* fix bugs

* add random dada

* fix bugs

* fix bugs

python/tests/ops/test_is_finite_op.py

@@ -0,0 +1,116 @@
+#!/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 numpy as np
+import unittest
+from op_test import OpTest, OpTestTool
+from op_test_helper import TestCaseHelper
+import paddle
+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 TestIsFiniteOp(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=-100,
+            high=100)
+
+        index = np.random.randint(0, len(self.x_np))
+        inf_data = np.where(self.x_np[index] > 0, np.inf, np.nan)
+        self.x_np[index] = inf_data.astype(self.case["x_dtype"])
+
+    def build_paddle_program(self, target):
+        x = paddle.to_tensor(self.x_np, stop_gradient=True)
+        out = paddle.isfinite(x)
+
+        self.paddle_outputs = [out]
+
+    def build_cinn_program(self, target):
+        builder = NetBuilder("is_finite")
+        x = builder.create_input(
+            self.nptype2cinntype(self.case["x_dtype"]), self.case["x_shape"],
+            "x")
+        out = builder.is_finite(x)
+
+        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(all_equal=True)
+
+
+class TestIsFiniteOpShape(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestIsFiniteOpShape"
+        self.cls = TestIsFiniteOp
+        self.inputs = [{
+            "x_shape": [1],
+        }, {
+            "x_shape": [1024],
+        }, {
+            "x_shape": [1, 2048],
+        }, {
+            "x_shape": [1, 1, 1],
+        }, {
+            "x_shape": [32, 64],
+        }, {
+            "x_shape": [16, 8, 4, 2],
+        }, {
+            "x_shape": [16, 8, 4, 2, 1],
+        }]
+        self.dtypes = [{
+            "x_dtype": "float32",
+        }]
+        self.attrs = []
+
+
+class TestIsFiniteOpDtype(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestIsFiniteOpDtype"
+        self.cls = TestIsFiniteOp
+        self.inputs = [{
+            "x_shape": [32, 64],
+        }]
+        self.dtypes = [{
+            "x_dtype": "int32",
+        }, {
+            "x_dtype": "int64",
+        }, {
+            "x_dtype": "float16",
+            "max_relative_error": 1e-3
+        }, {
+            "x_dtype": "float32",
+        }, {
+            "x_dtype": "float64",
+        }]
+        self.attrs = []
+
+
+if __name__ == "__main__":
+    TestIsFiniteOpShape().run()
+    TestIsFiniteOpDtype().run()

python/tests/ops/test_is_inf_op.py

@@ -0,0 +1,116 @@
+#!/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 numpy as np
+import unittest
+from op_test import OpTest, OpTestTool
+from op_test_helper import TestCaseHelper
+import paddle
+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 TestIsInfOp(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=-100,
+            high=100)
+
+        index = np.random.randint(0, len(self.x_np))
+        inf_data = np.zeros(self.x_np[index].shape, dtype="float") + np.inf
+        self.x_np[index] = inf_data.astype(self.case["x_dtype"])
+
+    def build_paddle_program(self, target):
+        x = paddle.to_tensor(self.x_np, stop_gradient=True)
+        out = paddle.isinf(x)
+
+        self.paddle_outputs = [out]
+
+    def build_cinn_program(self, target):
+        builder = NetBuilder("is_inf")
+        x = builder.create_input(
+            self.nptype2cinntype(self.case["x_dtype"]), self.case["x_shape"],
+            "x")
+        out = builder.is_inf(x)
+
+        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(all_equal=True)
+
+
+class TestIsInfOpShape(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestIsInfOpShape"
+        self.cls = TestIsInfOp
+        self.inputs = [{
+            "x_shape": [1],
+        }, {
+            "x_shape": [1024],
+        }, {
+            "x_shape": [1, 2048],
+        }, {
+            "x_shape": [1, 1, 1],
+        }, {
+            "x_shape": [32, 64],
+        }, {
+            "x_shape": [16, 8, 4, 2],
+        }, {
+            "x_shape": [16, 8, 4, 2, 1],
+        }]
+        self.dtypes = [{
+            "x_dtype": "float32",
+        }]
+        self.attrs = []
+
+
+class TestIsInfOpDtype(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestIsInfOpDtype"
+        self.cls = TestIsInfOp
+        self.inputs = [{
+            "x_shape": [32, 64],
+        }]
+        self.dtypes = [{
+            "x_dtype": "int32",
+        }, {
+            "x_dtype": "int64",
+        }, {
+            "x_dtype": "float16",
+            "max_relative_error": 1e-3
+        }, {
+            "x_dtype": "float32",
+        }, {
+            "x_dtype": "float64",
+        }]
+        self.attrs = []
+
+
+if __name__ == "__main__":
+    TestIsInfOpShape().run()
+    TestIsInfOpDtype().run()

python/tests/ops/test_is_nan_op.py

@@ -0,0 +1,116 @@
+#!/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 numpy as np
+import unittest
+from op_test import OpTest, OpTestTool
+from op_test_helper import TestCaseHelper
+import paddle
+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 TestIsNanOp(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=-100,
+            high=100)
+
+        index = np.random.randint(0, len(self.x_np))
+        nan_data = np.zeros(self.x_np[index].shape, dtype="float") + np.nan
+        self.x_np[index] = nan_data.astype(self.case["x_dtype"])
+
+    def build_paddle_program(self, target):
+        x = paddle.to_tensor(self.x_np, stop_gradient=True)
+        out = paddle.isnan(x)
+
+        self.paddle_outputs = [out]
+
+    def build_cinn_program(self, target):
+        builder = NetBuilder("is_nan")
+        x = builder.create_input(
+            self.nptype2cinntype(self.case["x_dtype"]), self.case["x_shape"],
+            "x")
+        out = builder.is_nan(x)
+
+        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(all_equal=True)
+
+
+class TestIsNanOpShape(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestIsNanOpShape"
+        self.cls = TestIsNanOp
+        self.inputs = [{
+            "x_shape": [1],
+        }, {
+            "x_shape": [1024],
+        }, {
+            "x_shape": [1, 2048],
+        }, {
+            "x_shape": [1, 1, 1],
+        }, {
+            "x_shape": [32, 64],
+        }, {
+            "x_shape": [16, 8, 4, 2],
+        }, {
+            "x_shape": [16, 8, 4, 2, 1],
+        }]
+        self.dtypes = [{
+            "x_dtype": "float32",
+        }]
+        self.attrs = []
+
+
+class TestIsNanOpDtype(TestCaseHelper):
+    def init_attrs(self):
+        self.class_name = "TestIsNanOpDtype"
+        self.cls = TestIsNanOp
+        self.inputs = [{
+            "x_shape": [32, 64],
+        }]
+        self.dtypes = [{
+            "x_dtype": "int32",
+        }, {
+            "x_dtype": "int64",
+        }, {
+            "x_dtype": "float16",
+            "max_relative_error": 1e-3
+        }, {
+            "x_dtype": "float32",
+        }, {
+            "x_dtype": "float64",
+        }]
+        self.attrs = []
+
+
+if __name__ == "__main__":
+    TestIsNanOpShape().run()
+    TestIsNanOpDtype().run()