Enable MKL-DNN for slim FP32 vs. INT8 tests (#18214)

* Enable MKL-DNN for slim FP32 vs. INT8 tests

test=develop

* added test for MobileNetV1 with MKL-DNN

test=develop

python/paddle/fluid/contrib/slim/tests/CMakeLists.txt

@@ -1,9 +1,10 @@
 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-function(inference_analysis_python_api_int8_test target model_dir data_dir filename)
+function(_inference_analysis_python_api_int8_test target model_dir data_dir filename use_mkldnn)
     py_test(${target} SRCS ${filename}
         ENVS CPU_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
+             FLAGS_use_mkldnn=${use_mkldnn}
         ARGS --infer_model ${model_dir}/model
              --infer_data ${data_dir}/data.bin
              --int8_model_save_path int8_models/${target}
@@ -11,6 +12,14 @@ function(inference_analysis_python_api_int8_test target model_dir data_dir filen
              --batch_size 50)
 endfunction()
 
+function(inference_analysis_python_api_int8_test target model_dir data_dir filename)
+    _inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir} ${filename} False)
+endfunction()
+
+function(inference_analysis_python_api_int8_test_mkldnn target model_dir data_dir filename)
+    _inference_analysis_python_api_int8_test(${target} ${model_dir} ${data_dir} ${filename} True)
+endfunction()
+
 function(inference_qat_int8_test target model_dir data_dir test_script use_mkldnn)
     py_test(${target} SRCS ${test_script}
             ENVS FLAGS_OMP_NUM_THREADS=${CPU_NUM_THREADS_ON_CI}
@@ -44,6 +53,7 @@ if(LINUX AND WITH_MKLDNN)
   # mobilenet int8
   set(INT8_MOBILENET_MODEL_DIR "${INT8_DATA_DIR}/mobilenetv1")
   inference_analysis_python_api_int8_test(test_slim_int8_mobilenet ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE})
+  inference_analysis_python_api_int8_test_mkldnn(test_slim_int8_mobilenet_mkldnn ${INT8_MOBILENET_MODEL_DIR} ${INT8_DATA_DIR} ${MKLDNN_INT8_TEST_FILE})
 
   # temporarily adding WITH_SLIM_MKLDNN_FULL_TEST FLAG for QA testing the following UTs locally,
   # since the following UTs cost too much time on CI test.

python/paddle/fluid/contrib/slim/tests/slim_int8_mkldnn_post_training_quantization.md

@@ -128,3 +128,4 @@ python ./test_mkldnn_int8_quantization_strategy.py --infer_model /PATH/TO/DOWNLO
 Notes:
 
 * The above commands will cost maybe several hours in the prediction stage (include int8 prediction and fp32 prediction) since there have 50000 pictures need to be predicted in `int8_full_val.bin`
+* Running the above command with environment variable `FLAGS_use_mkldnn=true` will make the FP32 part of the test running using MKL-DNN (the INT8 part uses MKL-DNN either way).

python/paddle/fluid/contrib/slim/tests/test_mkldnn_int8_quantization_strategy.py

@@ -23,6 +23,8 @@ import six
 import numpy as np
 import paddle
 import paddle.fluid as fluid
+from paddle.fluid.framework import IrGraph
+from paddle.fluid import core
 from paddle.fluid.contrib.slim.core import Compressor
 from paddle.fluid.log_helper import get_logger
 
@@ -112,6 +114,41 @@ class TestMKLDNNPostTrainingQuantStrategy(unittest.TestCase):
 
         return new_config_path
 
+    def _transform_depthwise_conv(self, graph):
+        '''
+        Transform depthwise_conv2d into conv2d, with MKL-DNN only
+        '''
+        ops = graph.all_op_nodes()
+        for op_node in ops:
+            name = op_node.name()
+            if name in ['depthwise_conv2d']:
+                input_var_node = graph._find_node_by_name(
+                    op_node.inputs, op_node.input("Input")[0])
+                weight_var_node = graph._find_node_by_name(
+                    op_node.inputs, op_node.input("Filter")[0])
+                output_var_node = graph._find_node_by_name(
+                    graph.all_var_nodes(), op_node.output("Output")[0])
+                attrs = {
+                    name: op_node.op().attr(name)
+                    for name in op_node.op().attr_names()
+                }
+
+                conv_op_node = graph.create_op_node(
+                    op_type='conv2d',
+                    attrs=attrs,
+                    inputs={
+                        'Input': input_var_node,
+                        'Filter': weight_var_node
+                    },
+                    outputs={'Output': output_var_node})
+
+                graph.link_to(input_var_node, conv_op_node)
+                graph.link_to(weight_var_node, conv_op_node)
+                graph.link_to(conv_op_node, output_var_node)
+                graph.safe_remove_nodes(op_node)
+
+        return graph
+
     def _predict(self, test_reader=None, model_path=None):
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
@@ -125,6 +162,13 @@ class TestMKLDNNPostTrainingQuantStrategy(unittest.TestCase):
                  fetch_targets] = fluid.io.load_inference_model(
                      model_path, exe, 'model', 'params')
 
+            use_mkldnn = bool(os.getenv("FLAGS_use_mkldnn", False))
+            if (use_mkldnn):
+                graph = IrGraph(
+                    core.Graph(inference_program.desc), for_test=True)
+                graph = self._transform_depthwise_conv(graph)
+                inference_program = graph.to_program()
+
             dshape = [3, 224, 224]
             top1 = 0.0
             top5 = 0.0