update 3 tests (#37922)

* update 3 tests

* fix typo error

paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc

-// Copyright (c) 2018 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.
@@ -130,7 +130,7 @@ ConvAffineChannelFusePass::ConvAffineChannelFusePass() {
       .IsType<std::vector<int>>()
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
       .End();
 
   AddOpCompat(OpCompat("affine_channel"))
@@ -148,7 +148,7 @@ ConvAffineChannelFusePass::ConvAffineChannelFusePass() {
       .IsTensor()
       .End()
       .AddAttr("data_layout")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
       .End();
 
   AddOpCompat(OpCompat("elementwise_add"))
@@ -197,19 +197,23 @@ void ConvAffineChannelFusePass::ApplyImpl(ir::Graph* graph) const {
 
     GET_CONV_BN_NODES(conv_ac_pattern);
 
+    // Get affine_channel bias for resizing eltwise_y!
+    auto* ac_bias_tensor =
+        scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
+
     // Create eltwise_y (conv bias) variable
     VarDesc eltwise_y_in_desc(
         patterns::PDNodeName(name_scope_, "eltwise_y_in"));
+    // Set shape && datatype manually
+    eltwise_y_in_desc.SetShape(framework::vectorize(ac_bias_tensor->dims()));
+    eltwise_y_in_desc.SetDataType(ac_bias_tensor->type());
+    eltwise_y_in_desc.SetLoDLevel(ac_bias->Var()->GetLoDLevel());
     eltwise_y_in_desc.SetPersistable(true);
+
+    // Initialize eltwise_y
     auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc);
     auto* eltwise_y_in_tensor =
         scope->Var(eltwise_y_in_node->Name())->GetMutable<LoDTensor>();
-
-    // Get affine_channel bias
-    auto* ac_bias_tensor =
-        scope->FindVar(ac_bias->Name())->GetMutable<LoDTensor>();
-
-    // Initialize eltwise_y
     eltwise_y_in_tensor->Resize(ac_bias_tensor->dims());
     std::fill_n(eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
                 eltwise_y_in_tensor->numel(), 0.0f);
@@ -278,7 +282,7 @@ ConvEltwiseAddAffineChannelFusePass::ConvEltwiseAddAffineChannelFusePass() {
       .IsType<std::vector<int>>()
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
       .End();
   AddOpCompat(OpCompat("affine_channel"))
       .AddInput("X")
@@ -295,7 +299,7 @@ ConvEltwiseAddAffineChannelFusePass::ConvEltwiseAddAffineChannelFusePass() {
       .IsTensor()
       .End()
       .AddAttr("data_layout")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
       .End();
   AddOpCompat(OpCompat("elementwise_add"))
       .AddInput("X")

paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc

-// Copyright (c) 2018 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.
@@ -57,7 +57,7 @@ ConvElementwiseAddFusePass::ConvElementwiseAddFusePass() {
       .AddAttr("dilations")
       .End()
       .AddAttr("data_format")
-      .IsStringIn({"NCHW", "NHWC", "AnyLayout"})
+      .IsStringIn({"NCHW" /*, "NHWC", "AnyLayout"*/})
       .End();
 
   AddOpCompat(OpCompat("elementwise_add"))
@@ -87,7 +87,7 @@ void ConvElementwiseAddFusePass::ApplyImpl(ir::Graph* graph) const {
 
   patterns::ConvElementwiseadd pattern(gpd.mutable_pattern(), pattern_name);
   pattern(x);
-
+  int found_conv_eltwise_count = 0;
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
     if (!IsCompat(subgraph, g)) {
@@ -135,9 +135,12 @@ void ConvElementwiseAddFusePass::ApplyImpl(ir::Graph* graph) const {
 
     // Delete the unneeded nodes.
     GraphSafeRemoveNodes(graph, {conv_op, conv_out, elementwise_add_op});
+    found_conv_eltwise_count++;
   };
 
   gpd(graph, handler);
+  // check if detect conv2d_fusion subgraph!
+  AddStatis(found_conv_eltwise_count);
 }
 
 }  // namespace ir

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

@@ -80,6 +80,7 @@ if (WITH_MKLDNN AND TENSORRT_FOUND AND WITH_GPU)
   set_tests_properties(test_fc_fuse_pass PROPERTIES TIMEOUT 240)
   set_tests_properties(test_simplify_with_basic_ops_pass_autoscan PROPERTIES TIMEOUT 60)
   set_tests_properties(test_adaptive_pool2d_convert_global_pass_autoscan PROPERTIES TIMEOUT 60)
+  set_tests_properties(test_conv_eltwiseadd_affine_channel_fuse_pass PROPERTIES TIMEOUT 100)
 endif()
 
 if (WITH_MKLDNN)

python/paddle/fluid/tests/unittests/ir/inference/test_conv_affine_channel_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.
@@ -12,216 +12,148 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
+from auto_scan_test import PassAutoScanTest, IgnoreReasons
+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 ConvAffineChannelFusePassExplicitPaddingTest(InferencePassTest):
-    def setUp(self):
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=[-1, 3, 64, 64], dtype="float32")
-            conv_out = fluid.layers.conv2d(
-                input=data,
-                num_filters=3,
-                filter_size=3,
-                groups=3,
-                padding=[1, 1, 1, 1],
-                bias_attr=False,
-                act=None)
-            input_scale = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            input_bias = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            ac_out = fluid.layers.affine_channel(
-                x=conv_out, scale=input_scale, bias=input_bias)
-
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [ac_out]
-
-    def test_check_output(self):
-        self.check_output()
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
-
-
-class ConvAffineChannelFusePassValidPaddingTest(InferencePassTest):
-    def setUp(self):
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=[-1, 3, 64, 64], dtype="float32")
-            conv_out = fluid.layers.conv2d(
-                input=data,
-                num_filters=3,
-                filter_size=3,
-                groups=3,
-                padding='VALID',
-                bias_attr=False,
-                act=None)
-            input_scale = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            input_bias = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            ac_out = fluid.layers.affine_channel(
-                x=conv_out, scale=input_scale, bias=input_bias)
-
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [ac_out]
-
-    def test_check_output(self):
-        self.check_output()
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
-
-
-class ConvAffineChannelFusePassSamePaddingTest(InferencePassTest):
-    def setUp(self):
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=[-1, 3, 64, 64], dtype="float32")
-            conv_out = fluid.layers.conv2d(
-                input=data,
-                num_filters=3,
-                filter_size=3,
-                groups=3,
-                padding='SAME',
-                bias_attr=False,
-                act=None)
-            input_scale = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            input_bias = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            ac_out = fluid.layers.affine_channel(
-                x=conv_out, scale=input_scale, bias=input_bias)
-
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [ac_out]
-
-    def test_check_output(self):
-        self.check_output()
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
-
-
-class ConvEltwiseAddAffineChannelFusePassExplicitPaddingTest(InferencePassTest):
-    def setUp(self):
-        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,
-                groups=3,
-                padding=[1, 1, 1, 1],
-                bias_attr=param_attr,
-                act=None)
-            input_scale = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            input_bias = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            ac_out = fluid.layers.affine_channel(
-                x=conv_out, scale=input_scale, bias=input_bias)
-
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [ac_out]
-
-    def test_check_output(self):
-        self.check_output()
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible(
-                'conv_eltwiseadd_affine_channel_fuse_pass'))
-
-
-class ConvEltwiseAddAffineChannelFusePassValidPaddingTest(InferencePassTest):
-    def setUp(self):
-        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,
-                groups=3,
-                padding='VALID',
-                bias_attr=param_attr,
-                act=None)
-            input_scale = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            input_bias = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            ac_out = fluid.layers.affine_channel(
-                x=conv_out, scale=input_scale, bias=input_bias)
-
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [ac_out]
-
-    def test_check_output(self):
-        self.check_output()
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible(
-                'conv_eltwiseadd_affine_channel_fuse_pass'))
-
-
-class ConvEltwiseAddAffineChannelFusePassSamePaddingTest(InferencePassTest):
-    def setUp(self):
-        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,
-                groups=3,
-                padding='Same',
-                bias_attr=param_attr,
-                act=None)
-            input_scale = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            input_bias = fluid.layers.create_parameter(
-                shape=[3], dtype="float32")
-            ac_out = fluid.layers.affine_channel(
-                x=conv_out, scale=input_scale, bias=input_bias)
-
-        self.feeds = {
-            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
-        }
-        self.fetch_list = [ac_out]
-
-    def test_check_output(self):
-        self.check_output()
-
-        self.assertTrue(
-            PassVersionChecker.IsCompatible(
-                'conv_eltwiseadd_affine_channel_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, reproduce_failure
+import hypothesis.strategies as st
+
+
+class TestConvAffineChannelFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_config(self, draw):
+        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
+        groups = draw(st.integers(min_value=1, max_value=3))
+        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
+        axis = draw(st.sampled_from([1]))
+        filter_channel = draw(st.integers(min_value=1, max_value=16)) * 4
+        filter_size = draw(st.integers(min_value=1, max_value=4))
+        in_channel = groups * filter_channel
+        out_channel_factor = draw(st.integers(min_value=1, max_value=16)) * 4
+        out_channel = groups * out_channel_factor
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+        dilations = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=2), min_size=2, max_size=2))
+        paddings = draw(
+            st.lists(
+                st.integers(
+                    min_value=0, max_value=2), min_size=2, max_size=2))
+        strides = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=2), min_size=2, max_size=2))
+        has_bias = draw(st.booleans())
+
+        x_shape = [
+            batch_size, in_channel, 64, 64
+        ] if data_format == "NCHW" else [batch_size, 64, 64, in_channel]
+        w_shape = [out_channel, filter_channel, filter_size, filter_size]
+        scale_shape = [out_channel]
+        bias_shape = [out_channel]
+
+        def generate_input():
+            return np.random.random(x_shape).astype(np.float32)
+
+        def generate_weight():
+            return np.random.random(w_shape).astype(np.float32)
+
+        def generate_bias():
+            return np.random.random(bias_shape).astype(np.float32)
+
+        def generate_scale_bias():
+            return np.random.random(bias_shape).astype(np.float32)
+
+        conv2d_op = OpConfig(
+            "conv2d",
+            inputs={
+                "Input": ["input_data"],
+                "Filter": ["conv2d_weight"],
+            },
+            outputs={"Output": ["conv_output"]},
+            data_format=data_format,
+            dilations=dilations,
+            padding_algorithm=padding_algorithm,
+            groups=groups,
+            paddings=paddings,
+            strides=strides,
+            has_bias=has_bias,
+            is_test=True)
+        ac_op = OpConfig(
+            "affine_channel",
+            inputs={
+                "X": ["conv_output"],
+                "Scale": ["affine_channel_scale"],
+                "Bias": ["affine_channel_bias"]
+            },
+            outputs={"Out": ["affine_channel_ouput"]},
+            data_layout=data_format)
+        if has_bias == True:
+            conv2d_op.inputs["Bias"] = ["conv2d_bias"]
+        ops = [conv2d_op, ac_op]
+
+        program_config = ProgramConfig(
+            ops=ops,
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input)),
+            },
+            weights={
+                "conv2d_weight":
+                TensorConfig(data_gen=partial(generate_weight)),
+                "affine_channel_scale":
+                TensorConfig(data_gen=partial(generate_scale_bias)),
+                "affine_channel_bias":
+                TensorConfig(data_gen=partial(generate_scale_bias)),
+            },
+            outputs=["affine_channel_ouput"])
+        if has_bias == True:
+            program_config.weights["conv2d_bias"] = TensorConfig(
+                data_gen=partial(generate_bias))
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_gpu=True)
+        yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
+
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
+
+    def add_ignore_pass_case(self):
+        # If the problem has been fixed, the judgment 
+        # in is_program_valid needs to be deleted!!!
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs['data_format'] == "NHWC":
+                return True
+            return False
+
+        # mkldnn Output has diff with bias!
+        def teller2(program_config, predictor_config):
+            return predictor_config.mkldnn_enabled() and program_config.ops[
+                0].attrs['has_bias'] == True
+
+        self.add_ignore_check_case(
+            teller1, IgnoreReasons.PASS_ACCURACY_ERROR,
+            "The output format of conv2d is wrong when data_format attribute is NHWC, \
+            because currently its fused op (Conv2DFusion) only supports data format of channel first (NCHW)."
+        )
+
+        self.add_ignore_check_case(
+            teller2, IgnoreReasons.PASS_ACCURACY_ERROR,
+            "Currently mkldnn Output has diff with bias!")
+
+    def test(self):
+        self.run_and_statis(
+            quant=False,
+            passes=["conv_affine_channel_fuse_pass"], )
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ir/inference/test_conv_elementwise_add_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.
@@ -12,41 +12,144 @@
 # 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, IgnoreReasons
+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
-from paddle.fluid.core import AnalysisConfig
-"""Test for fusion of conv and elementwise_add."""
-
-
-class ConvElementwiseAddFusePassTest(InferencePassTest):
-    def setUp(self):
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=[-1, 3, 100, 100], 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)
-
-        self.feeds = {
-            "data": np.random.random((1, 3, 100, 100)).astype("float32")
-        }
-        self.fetch_list = [conv_out]
-        self.enable_mkldnn = False
-
-    def test_check_output(self):
-        if core.is_compiled_with_cuda():
-            use_gpu = True
-            self.check_output_with_option(use_gpu)
-        self.assertTrue(
-            PassVersionChecker.IsCompatible('conv_elementwise_add_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
+
+
+class TestConvEltwiseAddFusePass(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 attrs[0]['data_format'] == "NHWC" and attrs[1]['axis'] != 3:
+            return False
+
+        return True
+
+    def sample_program_config(self, draw):
+        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
+        groups = draw(st.integers(min_value=1, max_value=3))
+        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
+        axis = draw(st.sampled_from([1]))
+        filter_channel = draw(st.integers(min_value=1, max_value=16)) * 4
+        filter_size = draw(st.integers(min_value=1, max_value=4))
+        in_channel = groups * filter_channel
+        out_channel_factor = draw(st.integers(min_value=1, max_value=16)) * 4
+        out_channel = groups * out_channel_factor
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+        dilations = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=2), min_size=2, max_size=2))
+        paddings = draw(
+            st.lists(
+                st.integers(
+                    min_value=0, max_value=2), min_size=2, max_size=2))
+        strides = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=2), min_size=2, max_size=2))
+
+        x_shape = [
+            batch_size, in_channel, 64, 64
+        ] if data_format == "NCHW" else [batch_size, 64, 64, in_channel]
+        w_shape = [out_channel, filter_channel, filter_size, filter_size]
+        scale_shape = [out_channel]
+        bias_shape = [out_channel]
+
+        def generate_input():
+            return np.random.random(x_shape).astype(np.float32)
+
+        def generate_weight():
+            return np.random.random(w_shape).astype(np.float32)
+
+        def generate_bias():
+            return np.random.random(bias_shape).astype(np.float32)
+
+        def generate_scale_bias():
+            return np.random.random(bias_shape).astype(np.float32)
+
+        conv2d_op = OpConfig(
+            "conv2d",
+            inputs={
+                "Input": ["input_data"],
+                "Filter": ["conv2d_weight"],
+            },
+            outputs={"Output": ["conv_output"]},
+            data_format=data_format,
+            dilations=dilations,
+            padding_algorithm=padding_algorithm,
+            groups=groups,
+            paddings=paddings,
+            strides=strides,
+            is_test=True)
+        eltwise_op = OpConfig(
+            "elementwise_add",
+            inputs={"X": ["conv_output"],
+                    "Y": ["conv2d_bias"]},
+            outputs={"Out": ["elementwise_output"]},
+            axis=axis)
+        ops = [conv2d_op, eltwise_op]
+
+        program_config = ProgramConfig(
+            ops=ops,
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input)),
+            },
+            weights={
+                "conv2d_weight":
+                TensorConfig(data_gen=partial(generate_weight)),
+                "conv2d_bias":
+                TensorConfig(data_gen=partial(generate_scale_bias)),
+            },
+            outputs=["elementwise_output"])
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_gpu=True)
+        yield config, ['conv2d_fusion'], (1e-4, 1e-4)
+
+        # # TRT
+        config = self.create_trt_inference_config()
+        config.enable_tensorrt_engine(
+            workspace_size=1 << 20,
+            max_batch_size=4,
+            min_subgraph_size=1,
+            precision_mode=paddle_infer.PrecisionType.Float32,
+            use_static=False,
+            use_calib_mode=False)
+        yield config, ['conv2d_fusion'], (1e-4, 1e-4)
+
+    def add_ignore_pass_case(self):
+        # If the problem has been fixed, the judgment 
+        # in is_program_valid needs to be deleted!!!
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs['data_format'] == "NHWC":
+                return True
+            return False
+
+        self.add_ignore_check_case(
+            teller1, IgnoreReasons.PASS_ACCURACY_ERROR,
+            "The output format of conv2d is wrong when data_format attribute is NHWC, \
+            it will trigger Broadcast dimension mismatch bug \
+            when data_format attribute is NHWC and axis of eltwise op is 1 for this pass."
+        )
+
+    def test(self):
+        self.run_and_statis(
+            quant=False,
+            passes=["conv_elementwise_add_fuse_pass"], )
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ir/inference/test_conv_eltwiseadd_affine_channel_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, IgnoreReasons
+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 TestConvEltwiseAddAffineChannelFusePass(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 attrs[0]['data_format'] == "NHWC" and attrs[1]['axis'] != 3:
+            return False
+
+        return True
+
+    def sample_program_config(self, draw):
+        padding_algorithm = draw(st.sampled_from(["EXPLICIT", "SAME", "VALID"]))
+        groups = draw(st.integers(min_value=1, max_value=3))
+        data_format = draw(st.sampled_from(["NCHW", "NHWC"]))
+        axis = draw(st.sampled_from([1]))
+        filter_channel = draw(st.integers(min_value=1, max_value=16)) * 4
+        filter_size = draw(st.integers(min_value=1, max_value=4))
+        in_channel = groups * filter_channel
+        out_channel_factor = draw(st.integers(min_value=1, max_value=16)) * 4
+        out_channel = groups * out_channel_factor
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+        dilations = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=2), min_size=2, max_size=2))
+        paddings = draw(
+            st.lists(
+                st.integers(
+                    min_value=0, max_value=2), min_size=2, max_size=2))
+        strides = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=2), min_size=2, max_size=2))
+        has_bias = draw(st.booleans())
+
+        x_shape = [
+            batch_size, in_channel, 64, 64
+        ] if data_format == "NCHW" else [batch_size, 64, 64, in_channel]
+        w_shape = [out_channel, filter_channel, filter_size, filter_size]
+        scale_shape = [out_channel]
+        bias_shape = [out_channel]
+
+        def generate_input():
+            return np.random.random(x_shape).astype(np.float32)
+
+        def generate_weight():
+            return np.random.random(w_shape).astype(np.float32)
+
+        def generate_bias():
+            return np.random.random(bias_shape).astype(np.float32)
+
+        def generate_scale_bias():
+            return np.random.random(bias_shape).astype(np.float32)
+
+        conv2d_op = OpConfig(
+            "conv2d",
+            inputs={
+                "Input": ["input_data"],
+                "Filter": ["conv2d_weight"],
+            },
+            outputs={"Output": ["conv_output"]},
+            data_format=data_format,
+            dilations=dilations,
+            padding_algorithm=padding_algorithm,
+            groups=groups,
+            paddings=paddings,
+            strides=strides,
+            has_bias=has_bias,
+            is_test=True)
+        eltwise_op = OpConfig(
+            "elementwise_add",
+            inputs={"X": ["conv_output"],
+                    "Y": ["conv2d_bias"]},
+            outputs={"Out": ["elementwise_output"]},
+            axis=axis)
+        ac_op = OpConfig(
+            "affine_channel",
+            inputs={
+                "X": ["elementwise_output"],
+                "Scale": ["affine_channel_scale"],
+                "Bias": ["affine_channel_bias"]
+            },
+            outputs={"Out": ["affine_channel_ouput"]},
+            data_layout=data_format)
+        if has_bias == True:
+            conv2d_op.inputs["Bias"] = ["conv2d_bias"]
+        ops = [conv2d_op, eltwise_op, ac_op]
+        program_config = ProgramConfig(
+            ops=ops,
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input)),
+            },
+            weights={
+                "conv2d_weight":
+                TensorConfig(data_gen=partial(generate_weight)),
+                "conv2d_bias": TensorConfig(data_gen=partial(generate_bias)),
+                "affine_channel_scale":
+                TensorConfig(data_gen=partial(generate_scale_bias)),
+                "affine_channel_bias":
+                TensorConfig(data_gen=partial(generate_scale_bias)),
+            },
+            outputs=["affine_channel_ouput"])
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_gpu=True)
+        yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
+
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
+
+        # TRT
+        config = self.create_trt_inference_config()
+        config.enable_tensorrt_engine(
+            workspace_size=1 << 20,
+            max_batch_size=4,
+            min_subgraph_size=1,
+            precision_mode=paddle_infer.PrecisionType.Float32,
+            use_static=False,
+            use_calib_mode=False)
+        yield config, ['conv2d', 'elementwise_add'], (1e-4, 1e-4)
+
+    def add_ignore_pass_case(self):
+        # If the problem has been fixed, the judgment 
+        # in is_program_valid needs to be deleted!!!
+        def teller1(program_config, predictor_config):
+            if program_config.ops[0].attrs['data_format'] == "NHWC":
+                return True
+            return False
+
+        # mkldnn Output has diff with bias!
+        def teller2(program_config, predictor_config):
+            return predictor_config.mkldnn_enabled() and program_config.ops[
+                0].attrs['has_bias'] == True
+
+        self.add_ignore_check_case(
+            teller1, IgnoreReasons.PASS_ACCURACY_ERROR,
+            "The output format of conv2d is wrong when data_format attribute is NHWC, \
+            it will trigger Broadcast dimension mismatch bug \
+            when data_format attribute is NHWC and axis of eltwise op is 1 for this pass."
+        )
+
+        self.add_ignore_check_case(
+            teller2, IgnoreReasons.PASS_ACCURACY_ERROR,
+            "Currently mkldnn Output has diff with bias!")
+
+    def test(self):
+        self.run_and_statis(
+            quant=False,
+            passes=["conv_eltwiseadd_affine_channel_fuse_pass"], )
+
+
+if __name__ == "__main__":
+    unittest.main()