Add unittests and OP version registry for quant_conv2d_dequant_fuse_pass (#27689)

paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.cc

@@ -19,6 +19,7 @@
 
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
 #include "paddle/fluid/framework/ir/quant_conv2d_dequant_fuse_pass.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -284,3 +285,15 @@ void QuantDequantFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(quant_conv2d_dequant_fuse_pass,
               paddle::framework::ir::QuantDequantFusePass);
+
+REGISTER_PASS_CAPABILITY(tensorrt_subgraph_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("fc", 0)
+            .LE("conv2d_transpose", 1)
+            .EQ("fake_quantize_abs_max", 0)
+            .EQ("fake_quantize_range_abs_max", 0)
+            .EQ("fake_quantize_moving_average_abs_max", 0)
+            .EQ("fake_channel_wise_quantize_abs_max", 0)
+            .EQ("fake_dequantize_max_abs", 0));

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

@@ -27,6 +27,10 @@ from paddle.fluid.core import PaddleDType
 from paddle.fluid.core import AnalysisConfig
 from paddle.fluid.core import create_paddle_predictor
 
+from paddle.fluid.framework import IrGraph
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
+from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
+
 
 class InferencePassTest(unittest.TestCase):
     def __init__(self, methodName='runTest'):
@@ -48,7 +52,8 @@ class InferencePassTest(unittest.TestCase):
     def _get_place(self):
         return set([False, core.is_compiled_with_cuda()])
 
-    def _save_models(self, executor, program):
+    def _save_models(self, executor, program, scope):
+        with fluid.scope_guard(scope):
             outs = executor.run(program=program,
                                 feed=self.feeds,
                                 fetch_list=self.fetch_list,
@@ -133,7 +138,11 @@ class InferencePassTest(unittest.TestCase):
         for place_ in use_gpu:
             self.check_output_with_option(place_, atol)
 
-    def check_output_with_option(self, use_gpu, atol=1e-5, flatten=False):
+    def check_output_with_option(self,
+                                 use_gpu,
+                                 atol=1e-5,
+                                 flatten=False,
+                                 quant=False):
         '''
         Check whether calculating on CPU and GPU, enable TensorRT 
         or disable TensorRT, enable MKLDNN or disable MKLDNN 
@@ -141,9 +150,52 @@ class InferencePassTest(unittest.TestCase):
         '''
         place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
         executor = fluid.Executor(place)
+        scope = fluid.Scope()
         device = "GPU" if use_gpu else "CPU"
+        with fluid.scope_guard(scope):
             executor.run(self.startup_program)
-        outs = self._save_models(executor, self.main_program)
+
+        if quant:
+            main_graph = IrGraph(
+                core.Graph(self.main_program.desc), for_test=True)
+
+            transform_pass = QuantizationTransformPass(
+                scope=scope,
+                place=place,
+                activation_quantize_type=self.activation_quant_type,
+                weight_quantize_type=self.weight_quant_type,
+                quantizable_op_type=[
+                    'conv2d', 'mul', 'depthwise_conv2d', 'conv2d_transpose'
+                ])
+            transform_pass.apply(main_graph)
+            weight_scale_map = {
+                "conv2d": "conv2d_0.w_0.scale",
+                "mul": "fc_0.w_0.scale"
+            }
+
+            weight_scale_tensor = scope.var(weight_scale_map[
+                self.quantized_op_type]).get_tensor()
+            weight_scale = np.ones(self.channels).astype("float32")
+            weight_scale_tensor.set(weight_scale, place)
+
+            op_nodes = main_graph.all_op_nodes()
+            for op_node in op_nodes:
+                if op_node.name() in [self.quantized_op_type, "relu"]:
+                    op_node.op()._set_attr("out_threshold", 0.5)
+
+            with fluid.scope_guard(scope):
+                executor.run(program=self.main_program,
+                             feed=self.feeds,
+                             fetch_list=self.fetch_list)
+
+            freeze_pass = QuantizationFreezePass(
+                scope=scope,
+                place=place,
+                weight_quantize_type=self.weight_quant_type)
+            freeze_pass.apply(main_graph)
+            self.main_program = main_graph.to_program()
+
+        outs = self._save_models(executor, self.main_program, scope)
 
         analysis_outputs = self._get_analysis_outputs(
             self._get_analysis_config(use_gpu=use_gpu))

python/paddle/fluid/tests/unittests/ir/inference/test_trt_quant_conv2d_dequant_fuse_pass.py

+# Copyright (c) 2020 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.
+
+import unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.framework import IrGraph
+from paddle.fluid.core import PassVersionChecker
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
+from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
+from paddle.fluid.core import AnalysisConfig
+
+
+class QuantDequantTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 32, 32], dtype="float32")
+            param_attr = fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=0.0),
+                trainable=False)
+            quantized_op_out = self.append_quantized_op(data, param_attr)
+            relu_out = fluid.layers.relu(quantized_op_out)
+        self.set_quant_pattern()
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 32, 32]).astype("float32"),
+        }
+        self.enable_trt = True
+        self.trt_parameters = QuantDequantTest.TensorRTParam(
+            1 << 30, 32, 0, AnalysisConfig.Precision.Int8, False, False)
+        self.fetch_list = [relu_out]
+
+    def append_quantized_op(self, x, param_attr):
+        return fluid.layers.conv2d(
+            input=x,
+            num_filters=3,
+            filter_size=3,
+            param_attr=param_attr,
+            bias_attr=False,
+            act=None)
+
+    def set_quant_pattern(self):
+        self.activation_quant_type = 'moving_average_abs_max'
+        self.weight_quant_type = 'channel_wise_abs_max'
+        self.quantized_op_type = 'conv2d'
+        self.channels = 3
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            use_gpu = True
+            self.check_output_with_option(use_gpu, flatten=True, quant=True)
+            self.assertTrue(
+                PassVersionChecker.IsCompatible(
+                    'quant_conv2d_dequant_fuse_pass'))
+
+
+class QuantFcDequantTest(QuantDequantTest):
+    def append_quantized_op(self, x, param_attr):
+        return fluid.layers.fc(x,
+                               size=100,
+                               num_flatten_dims=1,
+                               param_attr=param_attr,
+                               bias_attr=False,
+                               act=None)
+
+    def set_quant_pattern(self):
+        self.activation_quant_type = 'moving_average_abs_max'
+        self.weight_quant_type = 'abs_max'
+        self.quantized_op_type = 'mul'
+        self.channels = 1
+
+
+if __name__ == "__main__":
+    unittest.main()