update post_quantization.py (#255)

* update train.py

* update post_quantization.py

contrib/HumanSeg/utils/post_quantization.py

@@ -14,12 +14,14 @@
 
 from paddle.fluid.contrib.slim.quantization.quantization_pass import QuantizationTransformPass
 from paddle.fluid.contrib.slim.quantization.quantization_pass import AddQuantDequantPass
-from paddle.fluid.contrib.slim.quantization.quantization_pass import _op_real_in_out_name
+from paddle.fluid.contrib.slim.quantization.quantization_pass import _out_scale_op_list
 from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
+import utils.logging as logging
 import paddle.fluid as fluid
 import os
-
-import utils.logging as logging
+import re
+import numpy as np
+import time
 
 
 class HumanSegPostTrainingQuantization(PostTrainingQuantization):
@@ -42,7 +44,6 @@ class HumanSegPostTrainingQuantization(PostTrainingQuantization):
         fp32 model. It uses calibrate data to calculate the scale factor of
         quantized variables, and inserts fake quant/dequant op to obtain the
         quantized model.
-
         Args:
             executor(fluid.Executor): The executor to load, run and save the
                 quantized model.
@@ -76,6 +77,21 @@ class HumanSegPostTrainingQuantization(PostTrainingQuantization):
         Returns:
             None
         '''
+        self._support_activation_quantize_type = [
+            'range_abs_max', 'moving_average_abs_max', 'abs_max'
+        ]
+        self._support_weight_quantize_type = ['abs_max', 'channel_wise_abs_max']
+        self._support_algo_type = ['KL', 'abs_max', 'min_max']
+        self._support_quantize_op_type = \
+            list(set(QuantizationTransformPass._supported_quantizable_op_type +
+                AddQuantDequantPass._supported_quantizable_op_type))
+
+        # Check inputs
+        assert executor is not None, "The executor cannot be None."
+        assert batch_size > 0, "The batch_size should be greater than 0."
+        assert algo in self._support_algo_type, \
+            "The algo should be KL, abs_max or min_max."
+
         self._executor = executor
         self._dataset = dataset
         self._batch_size = batch_size
@@ -84,18 +100,19 @@ class HumanSegPostTrainingQuantization(PostTrainingQuantization):
         self._algo = algo
         self._is_use_cache_file = is_use_cache_file
         self._cache_dir = cache_dir
+        self._activation_bits = 8
+        self._weight_bits = 8
+        self._activation_quantize_type = 'range_abs_max'
+        self._weight_quantize_type = 'channel_wise_abs_max'
         if self._is_use_cache_file and not os.path.exists(self._cache_dir):
             os.mkdir(self._cache_dir)
 
-        supported_quantizable_op_type = \
-            QuantizationTransformPass._supported_quantizable_op_type + \
-            AddQuantDequantPass._supported_quantizable_op_type
         if is_full_quantize:
-            self._quantizable_op_type = supported_quantizable_op_type
+            self._quantizable_op_type = self._support_quantize_op_type
         else:
             self._quantizable_op_type = quantizable_op_type
             for op_type in self._quantizable_op_type:
-                assert op_type in supported_quantizable_op_type + \
+                assert op_type in self._support_quantize_op_type + \
                     AddQuantDequantPass._activation_type, \
                     op_type + " is not supported for quantization."
 
@@ -105,53 +122,72 @@ class HumanSegPostTrainingQuantization(PostTrainingQuantization):
         self._fetch_list = list(outputs.values())
         self._data_loader = None
 
-        self._op_real_in_out_name = _op_real_in_out_name
+        self._out_scale_op_list = _out_scale_op_list
         self._bit_length = 8
         self._quantized_weight_var_name = set()
         self._quantized_act_var_name = set()
         self._sampling_data = {}
-        self._quantized_var_scale_factor = {}
+        self._quantized_var_kl_threshold = {}
+        self._quantized_var_min = {}
+        self._quantized_var_max = {}
+        self._quantized_var_abs_max = {}
 
     def quantize(self):
         '''
         Quantize the fp32 model. Use calibrate data to calculate the scale factor of
         quantized variables, and inserts fake quant/dequant op to obtain the
         quantized model.
-
         Args:
             None
         Returns:
             the program of quantized model.
         '''
-        self._preprocess()
-
+        self._load_model_data()
+        self._collect_target_varnames()
+        self._set_activation_persistable()
+        batch_ct = 0
+        for data in self._data_loader():
+            batch_ct += 1
+            if self._batch_nums and batch_ct >= self._batch_nums:
+                break
         batch_id = 0
+        logging.info("Start to run batch!")
         for data in self._data_loader():
+            start = time.time()
             self._executor.run(
                 program=self._program,
                 feed=data,
                 fetch_list=self._fetch_list,
                 return_numpy=False)
-            self._sample_data(batch_id)
-
-            if batch_id % 5 == 0:
-                logging.info("run batch: {}".format(batch_id))
+            if self._algo == "KL":
+                self._sample_data(batch_id)
+            else:
+                self._sample_threshold()
+            end = time.time()
+            logging.debug(
+                '[Run batch data] Batch={}/{}, time_each_batch={} s.'.format(
+                    str(batch_id + 1), str(batch_ct), str(end - start)))
             batch_id += 1
             if self._batch_nums and batch_id >= self._batch_nums:
                 break
-        logging.info("all run batch: ".format(batch_id))
-        logging.info("calculate scale factor ...")
-        self._calculate_scale_factor()
-        logging.info("update the program ...")
-        self._update_program()
-
-        self._save_output_scale()
+        logging.info("All run batch: ".format(batch_id))
+        self._reset_activation_persistable()
+        logging.info("Calculate scale factor ...")
+        if self._algo == "KL":
+            self._calculate_kl_threshold()
+        logging.info("Update the program ...")
+        if self._algo in ["KL", "abs_max"]:
+            self._update_program()
+        else:
+            self._save_input_threhold()
+        logging.info("Save ...")
+        self._save_output_threshold()
+        logging.info("Finish quant!")
         return self._program
 
     def save_quantized_model(self, save_model_path):
         '''
         Save the quantized model to the disk.
-
         Args:
             save_model_path(str): The path to save the quantized model
         Returns:
@@ -166,59 +202,78 @@ class HumanSegPostTrainingQuantization(PostTrainingQuantization):
             params_filename='__params__',
             main_program=self._program)
 
-    def _preprocess(self):
+    def _load_model_data(self):
         '''
-        Load model and set data loader, collect the variable names for sampling,
-        and set activation variables to be persistable.
+        Set data loader.
         '''
         feed_vars = [fluid.framework._get_var(var.name, self._program) \
             for var in self._feed_list]
-
         self._data_loader = fluid.io.DataLoader.from_generator(
             feed_list=feed_vars, capacity=3 * self._batch_size, iterable=True)
         self._data_loader.set_sample_list_generator(
             self._dataset.generator(self._batch_size, drop_last=True),
             places=self._place)
 
-        # collect the variable names for sampling
-        persistable_var_names = []
-        for var in self._program.list_vars():
-            if var.persistable:
-                persistable_var_names.append(var.name)
-
-        for op in self._program.global_block().ops:
-            op_type = op.type
-            if op_type in self._quantizable_op_type:
-                if op_type in ("conv2d", "depthwise_conv2d"):
-                    self._quantized_act_var_name.add(op.input("Input")[0])
-                    self._quantized_weight_var_name.add(op.input("Filter")[0])
-                    self._quantized_act_var_name.add(op.output("Output")[0])
-                elif op_type == "mul":
-                    if self._is_input_all_not_persistable(
-                            op, persistable_var_names):
-                        op._set_attr("skip_quant", True)
-                        logging.warning(
-                            "Skip quant a mul op for two input variables are not persistable"
-                        )
-                    else:
-                        self._quantized_act_var_name.add(op.input("X")[0])
-                        self._quantized_weight_var_name.add(op.input("Y")[0])
-                        self._quantized_act_var_name.add(op.output("Out")[0])
-                else:
-                    # process other quantizable op type, the input must all not persistable
-                    if self._is_input_all_not_persistable(
-                            op, persistable_var_names):
-                        input_output_name_list = self._op_real_in_out_name[
-                            op_type]
-                        for input_name in input_output_name_list[0]:
-                            for var_name in op.input(input_name):
-                                self._quantized_act_var_name.add(var_name)
-                        for output_name in input_output_name_list[1]:
-                            for var_name in op.output(output_name):
-                                self._quantized_act_var_name.add(var_name)
+    def _calculate_kl_threshold(self):
+        '''
+        Calculate the KL threshold of quantized variables.
+        '''
+        assert self._algo == "KL", "The algo should be KL to calculate kl threshold."
+        ct = 1
+        # Abs_max threshold for weights
+        for var_name in self._quantized_weight_var_name:
+            start = time.time()
+            weight_data = self._sampling_data[var_name]
+            weight_threshold = None
+            if self._weight_quantize_type == "abs_max":
+                weight_threshold = np.max(np.abs(weight_data))
+            elif self._weight_quantize_type == "channel_wise_abs_max":
+                weight_threshold = []
+                for i in range(weight_data.shape[0]):
+                    abs_max_value = np.max(np.abs(weight_data[i]))
+                    weight_threshold.append(abs_max_value)
+            self._quantized_var_kl_threshold[var_name] = weight_threshold
+            end = time.time()
+            logging.debug(
+                '[Calculate weight] Weight_id={}/{}, time_each_weight={} s.'.
+                format(
+                    str(ct), str(len(self._quantized_weight_var_name)),
+                    str(end - start)))
+            ct += 1
 
-        # set activation variables to be persistable, so can obtain
-        # the tensor data in sample_data
-        for var in self._program.list_vars():
-            if var.name in self._quantized_act_var_name:
-                var.persistable = True
+        ct = 1
+        # KL threshold for activations
+        if self._is_use_cache_file:
+            for var_name in self._quantized_act_var_name:
+                start = time.time()
+                sampling_data = []
+                filenames = [f for f in os.listdir(self._cache_dir) \
+                    if re.match(var_name + '_[0-9]+.npy', f)]
+                for filename in filenames:
+                    file_path = os.path.join(self._cache_dir, filename)
+                    sampling_data.append(np.load(file_path))
+                    os.remove(file_path)
+                sampling_data = np.concatenate(sampling_data)
+                self._quantized_var_kl_threshold[var_name] = \
+                    self._get_kl_scaling_factor(np.abs(sampling_data))
+                end = time.time()
+                logging.debug(
+                    '[Calculate activation] Activation_id={}/{}, time_each_activation={} s.'
+                    .format(
+                        str(ct), str(len(self._quantized_act_var_name)),
+                        str(end - start)))
+                ct += 1
+        else:
+            for var_name in self._quantized_act_var_name:
+                start = time.time()
+                self._sampling_data[var_name] = np.concatenate(
+                    self._sampling_data[var_name])
+                self._quantized_var_kl_threshold[var_name] = \
+                    self._get_kl_scaling_factor(np.abs(self._sampling_data[var_name]))
+                end = time.time()
+                logging.debug(
+                    '[Calculate activation] Activation_id={}/{}, time_each_activation={} s.'
+                    .format(
+                        str(ct), str(len(self._quantized_act_var_name)),
+                        str(end - start)))
+                ct += 1