add mkldnn conv_elementwise_add_mkldnn_fuse_pass ut (#37612)

* add mkldnn conv_elementwise_add_mkldnn_fuse_pass ut

* update mkldnn conv_elementwise_add_mkldnn_fuse_pass ut

* update conv_elementwise_add_mkldnn_fuse_pass ut

* update conv_elementwise_add_mkldnn_fuse_pass ut

* update conv_elementwise_add_mkldnn_fuse_pass ut

* restrict conv2d data_format in conv_elementwise_add_mkldnn_fuse_pass

* update conv_elementwise_add_mkldnn_fuse_pass OpCompat

* update conv_elementwise_add_mkldnn_fuse_pass ut

* update ut

paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass.cc

@@ -117,7 +117,7 @@ ResidualConnectionMKLDNNFusePass::ResidualConnectionMKLDNNFusePass() {
       .IsType<std::vector<int>>()
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW", "AnyLayout"})
       .End();
 
   AddOpCompat(OpCompat("elementwise_add"))
@@ -131,7 +131,7 @@ ResidualConnectionMKLDNNFusePass::ResidualConnectionMKLDNNFusePass() {
       .IsTensor()
       .End()
       .AddAttr("axis")
-      .IsIntIn({-1, 0})
+      .IsIntIn({-1, 0, 1})
       .End();
 }
 

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

@@ -91,6 +91,7 @@ if (WITH_MKLDNN AND TENSORRT_FOUND AND WITH_GPU)
 endif()
 
 if (WITH_MKLDNN)
+  set_tests_properties(test_mkldnn_conv_elementwise_add_fuse_pass PROPERTIES TIMEOUT 120)
   set_tests_properties(test_mkldnn_depthwise_conv_pass PROPERTIES TIMEOUT 120)
   set_tests_properties(test_mkldnn_reshape_transpose_matmul_fuse_pass PROPERTIES TIMEOUT 100)
   set_tests_properties(test_mkldnn_prelu_op PROPERTIES TIMEOUT 300)

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

+# 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.
+# 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 auto_scan_test import PassAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig, OpConfig
+import numpy as np
+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
+
+
+class TestConvElementwiseAddMkldnnFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        # If the problem has been fixed, the judgment 
+        # needs to be deleted!!!
+        if attrs[1]['data_format'] == "NHWC":
+            return False
+
+        return True
+
+    def sample_program_config(self, draw):
+        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
+        dilations = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
+        groups = draw(st.sampled_from([1, 2, 4]))
+        paddings = draw(st.sampled_from([[0, 3], [1, 1], [1, 2, 3, 4]]))
+        strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        axis = draw(st.sampled_from([-1, 0, 1]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+
+        def generate_input1():
+            if data_format == "NCHW":
+                return np.random.random(
+                    [batch_size, 48, 64, 64]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [batch_size, 64, 64, 48]).astype(np.float32)
+
+        def generate_weight1():
+            return np.random.random(
+                [48, int(48 / groups), 3, 3]).astype(np.float32)
+
+        def compute_out_shape(padding_alg):
+            import paddle
+            import paddle.nn as nn
+
+            x_var = paddle.uniform(
+                (batch_size, 48, 64, 64), dtype='float32', min=-1., max=1.)
+            if padding_alg == "EXPLICIT":
+                conv = nn.Conv2D(48, 48, (3, 3), strides, paddings, dilations,
+                                 1)
+            else:
+                conv = nn.Conv2D(48, 48, (3, 3), strides, padding_alg,
+                                 dilations, 1)
+            y_var = conv(x_var)
+            return y_var.shape
+
+        def generate_weight2():
+            return np.random.random([48]).astype(np.float32)
+
+        if compute_out_shape(padding_algorithm) != (batch_size, 48, 64, 64):
+            axis = 1
+
+        relu_op = OpConfig(
+            type="relu",
+            inputs={"X": ["input_data1"]},
+            outputs={"Out": ["sigmoid_out"]},
+            attrs={})
+
+        conv2d_op = OpConfig(
+            type="conv2d",
+            inputs={"Input": ["sigmoid_out"],
+                    "Filter": ["conv_weight"]},
+            outputs={"Output": ["conv_output"]},
+            attrs={
+                "data_format": data_format,
+                "dilations": dilations,
+                "padding_algorithm": padding_algorithm,
+                "groups": groups,
+                "paddings": paddings,
+                "strides": strides
+            })
+
+        if axis == -1 or axis == 0:
+            elt_op = OpConfig(
+                type="elementwise_add",
+                inputs={"X": ["input_data1"],
+                        "Y": ["conv_output"]},
+                outputs={"Out": ["elementwise_output"]},
+                attrs={'axis': axis})
+        else:
+            elt_op = OpConfig(
+                type="elementwise_add",
+                inputs={"X": ["conv_output"],
+                        "Y": ["elementwise_weight"]},
+                outputs={"Out": ["elementwise_output"]},
+                attrs={'axis': axis})
+
+        model_net = [relu_op, conv2d_op, elt_op]
+
+        if axis == 1:
+            program_config = ProgramConfig(
+                ops=model_net,
+                weights={
+                    "conv_weight":
+                    TensorConfig(data_gen=partial(generate_weight1)),
+                    "elementwise_weight":
+                    TensorConfig(data_gen=partial(generate_weight2))
+                },
+                inputs={
+                    "input_data1":
+                    TensorConfig(data_gen=partial(generate_input1))
+                },
+                outputs=["elementwise_output"])
+        else:
+            program_config = ProgramConfig(
+                ops=model_net,
+                weights={
+                    "conv_weight":
+                    TensorConfig(data_gen=partial(generate_weight1))
+                },
+                inputs={
+                    "input_data1":
+                    TensorConfig(data_gen=partial(generate_input1))
+                },
+                outputs=["elementwise_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ["relu", "conv2d"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False, passes=["conv_elementwise_add_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()