update mkldnn conv_concat_relu_mkldnn_fuse_pass ut (#37606)

* update mkldnn conv_concat_relu_mkldnn_fuse_pass ut

* update conv_concat_relu_mkldnn_fuse_pass ut

* restrict conv2d data_format in conv_concat_relu_mkldnn_fuse_pass

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

@@ -59,7 +59,7 @@ ConvConcatReLUFusePass::ConvConcatReLUFusePass() {
       .IsType<std::vector<int>>()
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW"})
       .End();
 
   AddOpCompat(OpCompat("concat"))

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_conv_concat_relu_mkldnn_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.
@@ -12,80 +12,147 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import print_function
-
-import unittest
+from auto_scan_test import PassAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
 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 AnalysisConfig
-from paddle.fluid.core import PassVersionChecker
-
-
-class ConvConcatReluMkldnnFusePassTest_0(InferencePassTest):
-    def setUp(self):
-        self.set_params()
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data_1 = fluid.data(
-                name="data_1", shape=[-1, 3, 100, 100], dtype="float32")
-            data_2 = fluid.data(
-                name="data_2", shape=[-1, 3, 100, 100], dtype="float32")
-            conv_1 = fluid.layers.conv2d(
-                data_1,
-                num_filters=self.conv1_num_filters,
-                filter_size=self.conv1_filter_size,
-                padding=self.conv1_padding,
-                bias_attr=self.conv1_bias_attr)
-            conv_2 = fluid.layers.conv2d(
-                data_2,
-                num_filters=self.conv2_num_filters,
-                filter_size=self.conv2_filter_size,
-                padding=self.conv2_padding,
-                bias_attr=self.conv2_bias_attr)
-            concat = fluid.layers.concat(
-                [conv_1, conv_2], axis=self.concat_axis)
-            out = fluid.layers.relu(concat)
-
-        self.feeds = {
-            "data_1": np.random.random((1, 3, 100, 100)).astype("float32"),
-            "data_2": np.random.random((1, 3, 100, 100)).astype("float32")
+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 TestConvConcatReluMkldnnFusePass(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[0]['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, 2, 3, 4]]))
+        strides = draw(st.sampled_from([[1, 1], [2, 2], [1, 2]]))
+        axis = draw(st.sampled_from([0]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+
+        def generate_input(attrs):
+            if attrs[0]['data_format'] == "NCHW":
+                return np.random.random(
+                    [attrs[2]['batch_size'], 48, 64, 64]).astype(np.float32)
+            else:
+                return np.random.random(
+                    [attrs[2]['batch_size'], 64, 64, 48]).astype(np.float32)
+
+        def generate_weight():
+            return np.random.random(
+                [16, int(48 / groups), 3, 3]).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": "conv2d",
+            "op_inputs": {
+                "Input": ["input_data1"],
+                "Filter": ["input_weight"]
+            },
+            "op_outputs": {
+                "Output": ["conv1_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']
+            }
+        }, {
+            "op_type": "conv2d",
+            "op_inputs": {
+                "Input": ["input_data2"],
+                "Filter": ["input_weight"]
+            },
+            "op_outputs": {
+                "Output": ["conv2_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']
             }
-        self.fetch_list = [out]
-        self.enable_mkldnn = True
-
-    def set_params(self):
-        self.conv1_num_filters = 3
-        self.conv1_filter_size = 3
-        self.conv1_padding = 0
-        self.conv1_bias_attr = False
-        self.conv2_num_filters = 3
-        self.conv2_filter_size = 3
-        self.conv2_padding = 0
-        self.conv2_bias_attr = False
-        self.concat_axis = 0
-        self.pass_name = "conv_concat_relu_mkldnn_fuse_pass"
-
-    def test_check_output(self):
-        use_gpu = False
-        self.check_output_with_option(use_gpu)
-
-    def test_pass_compatible(self):
-        self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
-
-
-class ConvConcatReluMkldnnFusePassTest_1(ConvConcatReluMkldnnFusePassTest_0):
-    def set_params(self):
-        self.conv1_num_filters = 3
-        self.conv1_filter_size = 3
-        self.conv1_padding = 0
-        self.conv1_bias_attr = False
-        self.conv2_num_filters = 5
-        self.conv2_filter_size = 5
-        self.conv2_padding = 1
-        self.conv2_bias_attr = True
-        self.concat_axis = 1
-        self.pass_name = "conv_concat_relu_mkldnn_fuse_pass"
+        }, {
+            "op_type": "concat",
+            "op_inputs": {
+                "X": ["conv1_output", "conv2_output"]
+            },
+            "op_outputs": {
+                "Out": ["concat_output"]
+            },
+            "op_attrs": {
+                'axis': attrs[1]['axis']
+            }
+        }, {
+            "op_type": "relu",
+            "op_inputs": {
+                "X": ["concat_output"]
+            },
+            "op_outputs": {
+                "Out": ["relu_output"]
+            },
+            "op_attrs": {}
+        }]
+
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={
+                "input_weight": TensorConfig(data_gen=partial(generate_weight))
+            },
+            inputs={
+                "input_data1":
+                TensorConfig(data_gen=partial(generate_input, attrs)),
+                "input_data2":
+                TensorConfig(data_gen=partial(generate_input, attrs))
+            },
+            outputs=["relu_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ["conv2d", "conv2d", "concat"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False, passes=["conv_concat_relu_mkldnn_fuse_pass"])
 
 
 if __name__ == "__main__":