Add weight quantization in post_training_quanzitaion (#22445)

* support weight quantization in post_training_quanzitaion, test=develop
* add test for weight quantization, test=develop

paddle/fluid/operators/save_op.cc

@@ -90,4 +90,5 @@ REGISTER_OP_CPU_KERNEL(
     ops::SaveOpKernel<paddle::platform::CPUDeviceContext, double>,
     ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int>,
     ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int8_t>,
+    ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int16_t>,
     ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int64_t>);

python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py

@@ -16,10 +16,10 @@ import os
 import re
 import logging
 import numpy as np
-from ....executor import global_scope
 from .... import io
 from .... import core
 from .... import framework
+from ....executor import global_scope, Executor
 from ....framework import IrGraph
 from ....log_helper import get_logger
 from .quantization_pass import QuantizationTransformPass
@@ -27,12 +27,31 @@ from .quantization_pass import QuantizationFreezePass
 from .quantization_pass import AddQuantDequantPass
 from .quantization_pass import _op_real_in_out_name
 
-__all__ = ['PostTrainingQuantization']
+__all__ = ['PostTrainingQuantization', 'WeightQuantization']
 
 _logger = get_logger(
     __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
 
 
+def _load_variable_data(scope, var_name):
+    '''
+    Load variable value from scope
+    '''
+    return np.array(scope.find_var(var_name).get_tensor())
+
+
+def _set_variable_data(scope, place, var_name, np_value):
+    '''
+    Set the value of var node by name, if the node exits,
+    '''
+    assert isinstance(np_value, np.ndarray), \
+        'The type of value should be numpy array.'
+    var_node = scope.find_var(var_name)
+    if var_node != None:
+        tensor = var_node.get_tensor()
+        tensor.set(np_value, place)
+
+
 class PostTrainingQuantization(object):
     def __init__(self,
                  executor,
@@ -297,12 +316,12 @@ class PostTrainingQuantization(object):
         '''
         for var_name in self._quantized_weight_var_name:
             if var_name not in self._sampling_data:
-                var_tensor = self._load_var_value(var_name)
+                var_tensor = _load_variable_data(self._scope, var_name)
                 self._sampling_data[var_name] = var_tensor
 
         if self._is_use_cache_file:
             for var_name in self._quantized_act_var_name:
-                var_tensor = self._load_var_value(var_name)
+                var_tensor = _load_variable_data(self._scope, var_name)
                 var_tensor = var_tensor.ravel()
                 save_path = os.path.join(self._cache_dir,
                                          var_name + "_" + str(iter) + ".npy")
@@ -311,7 +330,7 @@ class PostTrainingQuantization(object):
             for var_name in self._quantized_act_var_name:
                 if var_name not in self._sampling_data:
                     self._sampling_data[var_name] = []
-                var_tensor = self._load_var_value(var_name)
+                var_tensor = _load_variable_data(self._scope, var_name)
                 var_tensor = var_tensor.ravel()
                 self._sampling_data[var_name].append(var_tensor)
 
@@ -397,11 +416,17 @@ class PostTrainingQuantization(object):
 
         # save scale factor to scale var node
         for key, val in self._quantized_var_scale_factor.items():
-            self._set_var_node_value(
-                key + ".scale", np.array(
+            _set_variable_data(
+                self._scope,
+                self._place,
+                key + ".scale",
+                np.array(
                     [val], dtype=np.float32))
-            self._set_var_node_value(
-                key + ".quant_dequant.scale", np.array(
+            _set_variable_data(
+                self._scope,
+                self._place,
+                key + ".quant_dequant.scale",
+                np.array(
                     [val], dtype=np.float32))
 
         # apply QuantizationFreezePass, and obtain the final quant model
@@ -430,23 +455,6 @@ class PostTrainingQuantization(object):
                                          self._quantized_var_scale_factor[
                                              output_var_name])
 
-    def _load_var_value(self, var_name):
-        '''
-        Load variable value from scope
-        '''
-        return np.array(self._scope.find_var(var_name).get_tensor())
-
-    def _set_var_node_value(self, var_node_name, np_value):
-        '''
-        Set the value of var node by name, if the node exits,
-        '''
-        assert isinstance(np_value, np.ndarray), \
-            'The type of value should be numpy array.'
-        var_node = self._scope.find_var(var_node_name)
-        if var_node != None:
-            tensor = var_node.get_tensor()
-            tensor.set(np_value, self._place)
-
     def _is_input_all_not_persistable(self, op, persistable_var_names):
         '''
         Analyze the real inputs of the op are all not persistable.
@@ -566,3 +574,132 @@ class PostTrainingQuantization(object):
                 tmp_sum1 += p_idx * (math.log(Q_sum * p_idx))
                 tmp_sum2 += p_idx * (math.log(P_sum * q_idx))
         return (tmp_sum1 - tmp_sum2) / P_sum
+
+
+class WeightQuantization(object):
+    _supported_quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
+
+    def __init__(self, model_dir, model_filename=None, params_filename=None):
+        '''
+        This class quantizes the weight of some ops to reduce the size of model
+        or improve the perforemace.
+
+        Args:
+            model_dir(str): The path of the fp32 model that will be quantized,
+                and the model and params files are under the path.
+            model_filename(str, optional): The name of file to load the inference
+                program. If it is None, the default filename '__model__' will
+                be used. Default is 'None'.
+            params_filename(str, optional): The name of file to load all parameters.
+                When all parameters were saved in a single binary file, set it
+                as the real filename. If parameters were saved in separate files,
+                set it as 'None'. Default is 'None'.
+        '''
+        self._model_dir = model_dir
+        self._model_filename = model_filename
+        self._params_filename = params_filename
+
+    def quantize_weight_to_int(self,
+                               save_model_dir,
+                               save_model_filename=None,
+                               save_params_filename=None,
+                               quantizable_op_type=["conv2d", "mul"],
+                               quantize_weight_bits=8,
+                               threshold_rate=0.0):
+        '''
+        In order to reduce the size of model, this api quantizes the weight
+        of some ops from float32 to int8/16. In the inference stage, the 
+        quantized weight will be dequantized to float32 again.
+        
+        Args:
+            save_model_dir(str): The path to save the quantized model.
+            save_model_filename(str, optional): The name of file to 
+                save the inference program. If it is None, the default 
+                filename '__model__' will be used. Default is 'None'.
+            save_params_filename(str, optional): The name of file to 
+                save all parameters. If it is None, parameters were 
+                saved in separate files. If it is not None, all 
+                parameters were saved in a single binary file.
+            quantizable_op_type(list[str], optional): The list of ops 
+                that will be quantized, and the quantized ops should be
+                contained in ["conv2d", "depthwise_conv2d", "mul"]. 
+                Default is ["conv2d","mul"].
+            quantize_weight_bits(int, optional): The bits for the quantized
+                weight, and it should be 8 or 16. Default is 8.
+            threshold_rate(float, optional): This api uses abs_max methd to 
+                quantize the weight from float32 to int8/16, and the abs max 
+                value is important for quantization diff. When the abs_max 
+                value is far away from the center of the numerical distribution, 
+                we can set threshold_rate between 1e-6 and 1e-8, so the abs max 
+                value will be optimized. Default is 0.0.
+        '''
+        for op_type in quantizable_op_type:
+            assert op_type in self._supported_quantizable_op_type, \
+                "input error:" + op_type + \
+                " is not supported for weight quantization."
+        assert quantize_weight_bits in [8, 16], \
+            "input error: quantize_weight_bits should be 8 or 16."
+        quantize_range = (1 << (quantize_weight_bits - 1)) - 1
+        save_weight_dtype = np.int8 if quantize_weight_bits == 8 else np.int16
+
+        place = core.CPUPlace()
+        exe = Executor(place)
+        scope = global_scope()
+        [program, feed_list, fetch_list] = \
+            io.load_inference_model(dirname=self._model_dir,
+                                    executor=exe,
+                                    model_filename=self._model_filename,
+                                    params_filename=self._params_filename)
+
+        persistable_var_names = []
+        for var in program.list_vars():
+            if var.persistable:
+                persistable_var_names.append(var.name)
+        for op in program.global_block().ops:
+            if op.type in quantizable_op_type:
+                for var_name in op.input_arg_names:
+                    if var_name in persistable_var_names:
+                        var_tensor_data = _load_variable_data(scope, var_name)
+                        if abs(threshold_rate) < 1e-10:
+                            threshold_value = np.max(np.abs(var_tensor_data))
+                        else:
+                            threshold_value = self._calculate_threshold(\
+                                var_tensor_data, threshold_rate)
+                            var_tensor_data[var_tensor_data >
+                                            threshold_value] = threshold_value
+                            var_tensor_data[var_tensor_data <
+                                            -threshold_value] = -threshold_value
+                        scale = threshold_value / quantize_range
+                        quantized_var_tensor_data = \
+                            np.around(var_tensor_data / scale)
+                        quantized_var_tensor_data = \
+                            quantized_var_tensor_data.astype(save_weight_dtype)
+                        _set_variable_data(scope, place, var_name,
+                                           quantized_var_tensor_data)
+                        op._set_attr(var_name + "_quant_scale", [scale])
+                        op._set_attr('quantize_weight_bits',
+                                     quantize_weight_bits)
+
+        io.save_inference_model(
+            dirname=save_model_dir,
+            feeded_var_names=feed_list,
+            target_vars=fetch_list,
+            executor=exe,
+            main_program=program,
+            model_filename=save_model_filename,
+            params_filename=save_params_filename)
+
+    def _calculate_threshold(self, input, threshold_rate, histogram_bins=5000):
+        input_abs = np.abs(input)
+        hist, hist_edeges = np.histogram(
+            input_abs, bins=histogram_bins, range=(0, np.max(input_abs)))
+        hist = hist / float(sum(hist))
+        hist_sum = 0
+        hist_index = 0
+        for i in range(len(hist)):
+            hist_sum += hist[i]
+            if hist_sum >= 1.0 - threshold_rate:
+                hist_index = i + 1
+                break
+        bin_width = hist_edeges[1] - hist_edeges[0]
+        return hist_index * bin_width

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

@@ -63,6 +63,7 @@ if(WIN32)
     list(REMOVE_ITEM TEST_OPS test_light_nas)
     list(REMOVE_ITEM TEST_OPS test_post_training_quantization_mobilenetv1)
     list(REMOVE_ITEM TEST_OPS test_post_training_quantization_resnet50)
+    list(REMOVE_ITEM TEST_OPS test_weight_quantization_mobilenetv1)
 endif()
 
 # int8 image classification python api test

python/paddle/fluid/contrib/slim/tests/test_weight_quantization_mobilenetv1.py

+# Copyright (c) 2019 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 os
+import time
+from paddle.dataset.common import download, DATA_HOME
+from paddle.fluid.contrib.slim.quantization import WeightQuantization
+
+
+class TestWeightQuantization(unittest.TestCase):
+    def setUp(self):
+        self.weight_quantization_dir = 'weight_quantization'
+        self.cache_folder = os.path.join(DATA_HOME,
+                                         self.weight_quantization_dir)
+
+    def download_model(self, model_name, data_url, data_md5):
+        download(data_url, self.weight_quantization_dir, data_md5)
+        file_name = data_url.split('/')[-1]
+        file_path = os.path.join(self.cache_folder, file_name)
+        print(model_name + ' is downloaded at ' + file_path)
+
+        unziped_path = os.path.join(self.cache_folder, model_name)
+        self.cache_unzipping(unziped_path, file_path)
+        print(model_name + ' is unziped at ' + unziped_path)
+        return unziped_path
+
+    def cache_unzipping(self, target_folder, zip_path):
+        if not os.path.exists(target_folder):
+            cmd = 'mkdir {0} && tar xf {1} -C {0}'.format(target_folder,
+                                                          zip_path)
+            os.system(cmd)
+
+    def run_test(self, model_name, model_data_url, model_data_md5,
+                 quantize_weight_bits, quantizable_op_type, threshold_rate):
+
+        model_dir = self.download_model(model_name, model_data_url,
+                                        model_data_md5)
+
+        timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
+        save_model_dir = os.path.join(
+            os.getcwd(),
+            model_name + "_wq_" + str(quantize_weight_bits) + "_" + timestamp)
+        weight_quant = WeightQuantization(model_dir=model_dir + "/model")
+        weight_quant.quantize_weight_to_int(
+            save_model_dir=save_model_dir,
+            quantize_weight_bits=quantize_weight_bits,
+            quantizable_op_type=quantizable_op_type,
+            threshold_rate=threshold_rate)
+        print("finish weight quantization for " + model_name + "\n")
+
+        try:
+            os.system("rm -rf {}".format(save_model_dir))
+        except Exception as e:
+            print("Failed to delete {} due to {}".format(save_model_dir, str(
+                e)))
+
+
+class TestWeightQuantizationMobilenetv1(TestWeightQuantization):
+    model_name = "mobilenetv1"
+    model_data_url = "http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz"
+    model_data_md5 = "13892b0716d26443a8cdea15b3c6438b"
+
+    def test_weight_quantization_mobilenetv1_8bit(self):
+        quantize_weight_bits = 8
+        quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
+        threshold_rate = 0.0
+        self.run_test(self.model_name, self.model_data_url, self.model_data_md5,
+                      quantize_weight_bits, quantizable_op_type, threshold_rate)
+
+    def test_weight_quantization_mobilenetv1_16bit(self):
+        quantize_weight_bits = 16
+        quantizable_op_type = ['conv2d', 'depthwise_conv2d', 'mul']
+        threshold_rate = 1e-9
+        self.run_test(self.model_name, self.model_data_url, self.model_data_md5,
+                      quantize_weight_bits, quantizable_op_type, threshold_rate)
+
+
+if __name__ == '__main__':
+    unittest.main()