add fuse_group python unitest (#22532)

* add fuse_group python unintest

python/paddle/fluid/tests/unittests/ir/CMakeLists.txt

 file(GLOB TEST_IR_PASSES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_IR_PASSES "${TEST_IR_PASSES}")
 
+if(NOT WITH_GPU OR WIN32 OR APPLE)
+  LIST(REMOVE_ITEM TEST_IR_PASSES test_ir_fusion_group)
+endif()
+
 foreach(target ${TEST_IR_PASSES})
   py_test_modules(${target} MODULES ${target})
 endforeach()

python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group.py

+#   Copyright (c) 2020 PaddlePaddle 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 pass_test import PassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+
+
+class FusionGroupPassTest(PassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data1 = fluid.data(name="data1", shape=[32, 128], dtype="float32")
+            data2 = fluid.data(name="data2", shape=[32, 128], dtype="float32")
+            data3 = fluid.data(name="data3", shape=[32, 128], dtype="float32")
+            tmp_1 = fluid.layers.elementwise_add(data1, data2)
+            tmp_2 = fluid.layers.elementwise_mul(data3, tmp_1)
+
+        self.feeds = {
+            "data1": np.random.random((32, 128)).astype("float32"),
+            "data2": np.random.random((32, 128)).astype("float32"),
+            "data3": np.random.random((32, 128)).astype("float32")
+        }
+        self.fetch_list = [tmp_1, tmp_2]
+        self.pass_names = "fusion_group_pass"
+        self.fused_op_type = "fusion_group"
+        self.num_fused_ops = 1
+
+    def test_check_output(self):
+        use_gpu_set = []
+        if core.is_compiled_with_cuda():
+            use_gpu_set.append(True)
+        for use_gpu in use_gpu_set:
+            self.pass_attrs = {"fusion_group_pass": {"use_gpu": use_gpu}}
+            place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
+            self.check_output_with_place(place, startup_on_cpu=False)
+
+
+class FusionGroupPassTest1(FusionGroupPassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = []
+            for i in range(5):
+                data.append(
+                    fluid.data(
+                        name=("data" + str(i)),
+                        shape=[32, 128],
+                        dtype="float32"))
+            tmp_1 = (
+                fluid.layers.assign(data[0]) * fluid.layers.sigmoid(data[1])
+            ) + (fluid.layers.sigmoid(data[2]) * fluid.layers.tanh(data[3]))
+            tmp_2 = fluid.layers.tanh(tmp_1) + fluid.layers.sigmoid(data[4])
+
+        self.feeds = {}
+        for i in range(5):
+            self.feeds["data" + str(i)] = np.random.random(
+                (32, 128)).astype("float32")
+
+        self.fetch_list = [tmp_1, tmp_2]
+        self.pass_names = "fusion_group_pass"
+        self.fused_op_type = "fusion_group"
+        self.num_fused_ops = 1
+
+
+class FusionGroupPassTest2(FusionGroupPassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = []
+            for i in range(3):
+                data.append(
+                    fluid.data(
+                        name=("data" + str(i)),
+                        shape=[32, 128],
+                        dtype="float32"))
+            data.append(
+                fluid.data(
+                    name="data3", shape=[128, 32], dtype="float32"))
+            tmp_1 = fluid.layers.relu((data[0] - data[1]) * data[2])
+            tmp_2 = fluid.layers.sigmoid(data[3])
+            tmp_3 = fluid.layers.relu(tmp_2)
+            tmp_4 = fluid.layers.mul(tmp_1, tmp_3)
+
+        self.feeds = {}
+        for i in range(3):
+            self.feeds["data" + str(i)] = np.random.random(
+                (32, 128)).astype("float32")
+        self.feeds["data3"] = np.random.random((128, 32)).astype("float32")
+
+        self.fetch_list = [tmp_1, tmp_2, tmp_3, tmp_4]
+        self.pass_names = "fusion_group_pass"
+        self.fused_op_type = "fusion_group"
+        self.num_fused_ops = 2
+
+
+if __name__ == "__main__":
+    unittest.main()