add mkldnn conv3d_bias_mkldnn_fuse_pass ut (#37700)

* add mkldnn conv3d_bias_mkldnn_fuse_pass ut

* update conv3d_bias_mkldnn_fuse_pass ut

* disable conv3d_bias_mkldnn_fuse_pass

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

@@ -147,7 +147,21 @@ Conv3DBiasFusePass::Conv3DBiasFusePass() {
       .IsType<std::vector<int>>()
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "NHWC"})
+      .IsStringIn({"NDHWC", "NCDHW"})
+      .End();
+
+  AddOpCompat(OpCompat("elementwise_add"))
+      .AddInput("X")
+      .IsTensor()
+      .End()
+      .AddInput("Y")
+      .IsTensor()
+      .End()
+      .AddOutput("Out")
+      .IsTensor()
+      .End()
+      .AddAttr("axis")
+      .IsNumGE(1)
       .End();
 }
 

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -241,7 +241,8 @@ void CpuPassStrategy::EnableMKLDNN() {
              "conv_transpose_eltwiseadd_bn_fuse_pass",    //
              "conv_bias_mkldnn_fuse_pass",                //
              "conv_transpose_bias_mkldnn_fuse_pass",
-             "conv3d_bias_mkldnn_fuse_pass",  //
+             // TODO(baoachun): Need to support 5-dimensional input.
+             // "conv3d_bias_mkldnn_fuse_pass",  //
              "conv_elementwise_add_mkldnn_fuse_pass",
              "conv_concat_relu_mkldnn_fuse_pass",
              "conv_relu_mkldnn_fuse_pass",          //

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv3d_bias_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
+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 TestConv3dBiasMkldnnFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_config(self, draw):
+        data_format = draw(st.sampled_from(["NCDHW", "NDHWC"]))
+        dilations = draw(st.sampled_from([[1, 1, 1], [2, 2, 2], [1, 2, 1]]))
+        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
+        groups = draw(st.sampled_from([1, 2, 4]))
+        paddings = draw(st.sampled_from([[0, 3, 2], [1, 2, 3, 4, 3, 1]]))
+        strides = draw(st.sampled_from([[1, 1, 1], [2, 2, 2], [1, 2, 1]]))
+        axis = draw(st.sampled_from([1]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+
+        def generate_input1(attrs):
+            if attrs[0]['data_format'] == "NCDHW":
+                return np.random.random(
+                    [attrs[2]['batch_size'], 48, 64, 32, 64]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [attrs[2]['batch_size'], 64, 32, 64, 48]).astype(np.float32)
+
+        def generate_weight1():
+            return np.random.random(
+                [16, int(48 / groups), 3, 3, 3]).astype(np.float32)
+
+        def generate_weight2():
+            return np.random.random([16]).astype(np.float32)
+
+        attrs = [{
+            "data_format": data_format,
+            "dilations": dilations,
+            "padding_algorithm": padding_algorithm,
+            "groups": groups,
+            "paddings": paddings,
+            "strides": strides
+        }, {
+            "axis": axis
+        }, {
+            'batch_size': batch_size
+        }]
+
+        ops_config = [{
+            "op_type": "conv3d",
+            "op_inputs": {
+                "Input": ["input_data1"],
+                "Filter": ["conv_weight"]
+            },
+            "op_outputs": {
+                "Output": ["conv_output"]
+            },
+            "op_attrs": {
+                "data_format": attrs[0]['data_format'],
+                "dilations": attrs[0]['dilations'],
+                "padding_algorithm": attrs[0]['padding_algorithm'],
+                "groups": attrs[0]['groups'],
+                "paddings": attrs[0]['paddings'],
+                "strides": attrs[0]['strides'],
+                "is_test": True
+            }
+        }, {
+            "op_type": "elementwise_add",
+            "op_inputs": {
+                "X": ["conv_output"],
+                "Y": ["elementwise_weight"]
+            },
+            "op_outputs": {
+                "Out": ["elementwise_output"]
+            },
+            "op_attrs": {
+                'axis': attrs[1]['axis']
+            },
+        }]
+
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            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, attrs))
+            },
+            outputs=["elementwise_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ["conv3d"], (1e-5, 1e-5)
+
+    # TODO(baoachun)
+    # Need to support 5-dimensional input when using mkldnn.
+    def test(self):
+        pass
+        # self.run_and_statis(
+        #     quant=False, passes=["conv3d_bias_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()