update repeated_fc_relu_fuse_pass ut (#37845)

* update repeated_fc_relu_fuse_pass ut

* update ut

python/paddle/fluid/tests/unittests/ir/inference/test_repeated_fc_relu_fuse_pass.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2021 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.
@@ -12,82 +12,116 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
+from auto_scan_test import PassAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig, OpConfig
 import numpy as np
-from inference_pass_test import InferencePassTest
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-from paddle.fluid.core import PassVersionChecker
-
-
-class RepeatedFcReluFusePass3Test(InferencePassTest):
-    def setUp(self):
-        fc_num = 3
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=[-1, 3, 64, 64], dtype="float32")
-            param_attr = fluid.ParamAttr(
-                initializer=fluid.initializer.Xavier(uniform=False),
-                learning_rate=0.001)
-            conv_out = fluid.layers.conv2d(
-                input=data,
-                num_filters=3,
-                filter_size=3,
-                bias_attr=param_attr,
-                act=None)
-            fc_outs = []
-            fc_outs.append(
-                fluid.layers.fc(input=[conv_out], act="relu", size=1000))
-            for i in range(1, fc_num):
-                fc_outs.append(
-                    fluid.layers.fc(
-                        input=[fc_outs[i - 1]], act="relu", size=1000))
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [fc_outs[fc_num - 1]]
-
-    def test_check_output(self):
-        use_gpu = False
-        self.check_output_with_option(use_gpu)
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible('repeated_fc_relu_fuse_pass'))
-
-
-class RepeatedFcReluFusePass9Test(InferencePassTest):
-    def setUp(self):
-        fc_num = 9
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=[-1, 3, 64, 64], dtype="float32")
-            param_attr = fluid.ParamAttr(
-                initializer=fluid.initializer.Xavier(uniform=False),
-                learning_rate=0.001)
-            conv_out = fluid.layers.conv2d(
-                input=data,
-                num_filters=3,
-                filter_size=3,
-                bias_attr=param_attr,
-                act=None)
-            fc_outs = []
-            fc_outs.append(
-                fluid.layers.fc(input=[conv_out], act="relu", size=1000))
-            for i in range(1, fc_num):
-                fc_outs.append(
-                    fluid.layers.fc(
-                        input=[fc_outs[i - 1]], act="relu", size=1000))
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [fc_outs[fc_num - 1]]
-
-    def test_check_output(self):
-        use_gpu = False
-        self.check_output_with_option(use_gpu)
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible('repeated_fc_relu_fuse_pass'))
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+import unittest
+
+import hypothesis
+from hypothesis import given, settings, seed, example, assume
+import hypothesis.strategies as st
+from functools import reduce
+
+
+class TestRepeatedFcReluFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_config(self, draw):
+        x_col = draw(st.sampled_from([1]))
+        y_col = draw(st.sampled_from([1]))
+        axis = draw(st.sampled_from([-1, 1]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+        dim = draw(st.sampled_from([32, 64, 128]))
+
+        def generate_input():
+            return np.random.random([batch_size, dim]).astype(np.float32)
+
+        def generate_weight(shape):
+            return np.random.random(shape).astype(np.float32)
+
+        attrs = [{
+            "x_col": x_col,
+            "y_col": y_col
+        }, {
+            "axis": axis
+        }, {
+            'batch_size': batch_size,
+            'dim': dim
+        }]
+
+        mul_op1 = OpConfig(
+            type="mul",
+            inputs={"X": ["input_data"],
+                    "Y": ["mul1_weight"]},
+            outputs={"Out": ["mul1_output"]},
+            attrs={"x_num_col_dims": x_col,
+                   "y_num_col_dims": y_col})
+
+        elt_op1 = OpConfig(
+            type="elementwise_add",
+            inputs={"X": ["mul1_output"],
+                    "Y": ["elementwise1_weight"]},
+            outputs={"Out": ["elementwise1_output"]},
+            attrs={"axis": axis})
+
+        relu_op1 = OpConfig(
+            type="relu",
+            inputs={"X": ["elementwise1_output"]},
+            outputs={"Out": ["relu1_output"]},
+            attrs={})
+
+        mul_op2 = OpConfig(
+            type="mul",
+            inputs={"X": ["relu1_output"],
+                    "Y": ["mul2_weight"]},
+            outputs={"Out": ["mul2_output"]},
+            attrs={"x_num_col_dims": x_col,
+                   "y_num_col_dims": y_col})
+
+        elt_op2 = OpConfig(
+            type="elementwise_add",
+            inputs={"X": ["mul2_output"],
+                    "Y": ["elementwise2_weight"]},
+            outputs={"Out": ["elementwise2_output"]},
+            attrs={"axis": axis})
+
+        relu_op2 = OpConfig(
+            type="relu",
+            inputs={"X": ["elementwise2_output"]},
+            outputs={"Out": ["relu2_output"]},
+            attrs={})
+
+        model_net = [mul_op1, elt_op1, relu_op1, mul_op2, elt_op2, relu_op2]
+
+        program_config = ProgramConfig(
+            ops=model_net,
+            weights={
+                "mul1_weight": TensorConfig(data_gen=partial(generate_weight,
+                                                             [dim, 32])),
+                "mul2_weight":
+                TensorConfig(data_gen=partial(generate_weight, [32, 128])),
+                "elementwise1_weight":
+                TensorConfig(data_gen=partial(generate_weight, [32])),
+                "elementwise2_weight":
+                TensorConfig(data_gen=partial(generate_weight, [128]))
+            },
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input)),
+            },
+            outputs=["relu2_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config()
+        yield config, ["fusion_repeated_fc_relu"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(passes=["repeated_fc_relu_fuse_pass"])
 
 
 if __name__ == "__main__":