Add fused_batch_norm_add_relu and fix a bug.

Author: Xreki

api/deploy/collect_api_info.py

@@ -72,7 +72,7 @@ def _import_api(test_module_name, basename):
     for filename in sorted(os.listdir(tests_path)):
         api_name = os.path.splitext(filename)[0]
         file_extension = os.path.splitext(filename)[1]
-        if file_extension == '.py' and _is_special_module(api_name):
+        if file_extension == '.py' and not _is_special_module(api_name):
             module = _import_api(test_module_name, api_name)
             if module:
                 test_cases_dict[api_name] = module
@@ -138,7 +138,7 @@ def main(args):
     parser.add_argument(
         '--test_module_name',
         type=str,
-        default="tests",
+        default="tests_v2",
         help='The module_name under benchmark/api (tests|tests_v2|dynamic_tests_v2).'
     )
     parser.add_argument(

api/tests_v2/fused_batch_norm_add_relu.py

@@ -0,0 +1,95 @@
+#   Copyright (c) 2022 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.
+
+from common_import import *
+from batch_norm import BatchNormConfig
+
+
+class FusedBatchNormAddReluConfig(BatchNormConfig):
+    def __init__(self):
+        super(FusedBatchNormAddReluConfig,
+              self).__init__("fused_batch_norm_add_relu")
+        self.alias_name = "batch_norm"
+
+
+class PDFusedBatchNormAddRelu(PaddleAPIBenchmarkBase):
+    def build_program(self, config):
+        def _create_parameter(name, value, stop_gradient):
+            param = paddle.create_parameter(
+                name=name,
+                shape=[config.num_channels],
+                dtype=config.x_dtype,
+                attr=paddle.ParamAttr(
+                    initializer=paddle.nn.initializer.Constant(value)))
+            param.stop_gradient = stop_gradient
+            return param
+
+        x = self.variable(name='x', shape=config.x_shape, dtype=config.x_dtype)
+        y = self.variable(name='y', shape=config.x_shape, dtype=config.x_dtype)
+
+        running_mean = _create_parameter(
+            name='running_mean', value=0.5, stop_gradient=True)
+        running_var = _create_parameter(
+            name='running_var', value=0.1, stop_gradient=True)
+
+        scale = _create_parameter(name='scale', value=0.5, stop_gradient=False)
+        bias = _create_parameter(name='bias', value=0.1, stop_gradient=False)
+
+        bn_out = paddle.nn.functional.batch_norm(
+            x=x,
+            running_mean=running_mean,
+            running_var=running_var,
+            weight=scale,
+            bias=bias,
+            epsilon=config.epsilon,
+            momentum=config.momentum,
+            training=config.training,
+            data_format=config.data_format)
+        add_out = bn_out + y
+        relu_out = paddle.nn.functional.relu(add_out)
+
+        self.feed_vars = [x, y]
+        self.fetch_vars = [bn_out, add_out, relu_out]
+        if config.backward:
+            self.append_gradients(relu_out, [x, scale, bias, bn_out, add_out])
+
+
+class TFFusedBatchNormAddRelu(TensorflowAPIBenchmarkBase):
+    def build_graph(self, config):
+        x = self.variable(name='x', shape=config.x_shape, dtype=config.x_dtype)
+        y = self.variable(name='y', shape=config.x_shape, dtype=config.x_dtype)
+        bn = tf.keras.layers.BatchNormalization(
+            axis=config.axis,
+            momentum=config.momentum,
+            epsilon=config.epsilon,
+            beta_initializer=tf.constant_initializer(0.1),
+            gamma_initializer=tf.constant_initializer(0.5),
+            moving_mean_initializer=tf.constant_initializer(0.5),
+            moving_variance_initializer=tf.constant_initializer(0.1))
+        bn_out = bn(x, training=config.training)
+        add_out = bn_out + y
+        relu_out = tf.nn.relu(add_out)
+
+        self.feed_list = [x, y]
+        self.fetch_list = [bn_out, add_out, relu_out]
+        if config.backward:
+            self.append_gradients(relu_out,
+                                  [x, bn.gamma, bn.beta, bn_out, add_out])
+
+
+if __name__ == '__main__':
+    test_main(
+        PDFusedBatchNormAddRelu(),
+        TFFusedBatchNormAddRelu(),
+        config=FusedBatchNormAddReluConfig())