[cherry-pick]Add progress bar and speed up Quantization Pass (#43454)

* Add progress bar and speed up Quantization Pass

* fix typo

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

@@ -17,6 +17,10 @@ import re
 import logging
 import numpy as np
 import shutil
+try:
+    from tqdm import tqdm
+except:
+    from .utils import tqdm
 from inspect import isgeneratorfunction
 from .... import io
 from .... import core
@@ -357,38 +361,40 @@ class PostTrainingQuantization(object):
         self._set_activation_persistable()
 
         if self._algo in ["KL", "hist"]:
-            _logger.info("Preparation stage ...")
             batch_id = 0
+            with tqdm(
+                    total=self._batch_nums,
+                    bar_format='Preparation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
+                    ncols=80) as t:
+                for data in self._data_loader():
+                    self._executor.run(program=self._program,
+                                       feed=data,
+                                       fetch_list=self._fetch_list,
+                                       return_numpy=False,
+                                       scope=self._scope)
+                    self._collect_activation_abs_min_max()
+                    batch_id += 1
+                    t.update()
+                    if self._batch_nums and batch_id >= self._batch_nums:
+                        break
+            self._init_sampling_act_histogram()
+
+        batch_id = 0
+        with tqdm(
+                total=self._batch_nums,
+                bar_format='Sampling stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
+                ncols=80) as t:
             for data in self._data_loader():
                 self._executor.run(program=self._program,
                                    feed=data,
                                    fetch_list=self._fetch_list,
                                    return_numpy=False,
                                    scope=self._scope)
-                self._collect_activation_abs_min_max()
-                if batch_id % 5 == 0:
-                    _logger.info("Run batch: " + str(batch_id))
+                self._sampling()
                 batch_id += 1
+                t.update()
                 if self._batch_nums and batch_id >= self._batch_nums:
                     break
-            _logger.info("Finish preparation stage, all batch:" + str(batch_id))
-            self._init_sampling_act_histogram()
-
-        _logger.info("Sampling stage ...")
-        batch_id = 0
-        for data in self._data_loader():
-            self._executor.run(program=self._program,
-                               feed=data,
-                               fetch_list=self._fetch_list,
-                               return_numpy=False,
-                               scope=self._scope)
-            self._sampling()
-            if batch_id % 5 == 0:
-                _logger.info("Run batch: " + str(batch_id))
-            batch_id += 1
-            if self._batch_nums and batch_id >= self._batch_nums:
-                break
-        _logger.info("Finish sampling stage, all batch: " + str(batch_id))
 
         if self._algo == 'avg':
             for var_name in self._quantized_act_var_name:
@@ -823,8 +829,9 @@ class PostTrainingQuantization(object):
             min_value = float(np.min(var_tensor))
             max_value = float(np.max(var_tensor))
             if var_name not in self._sampling_act_abs_min_max:
-                self._sampling_act_abs_min_max[
-                    var_name] = [min_value, max_value]
+                self._sampling_act_abs_min_max[var_name] = [
+                    min_value, max_value
+                ]
             else:
                 if min_value < self._sampling_act_abs_min_max[var_name][0]:
                     self._sampling_act_abs_min_max[var_name][0] = min_value

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

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import sys
 import numpy as np
 from ....framework import IrNode
 from ....framework import Operator
@@ -52,7 +53,6 @@ _act_supported_quantizable_op_type = [
     "leaky_relu",
     "tanh",
     "swish",
-    "scale",
     "transpose",
     "transpose2",
     "sigmoid",
@@ -162,7 +162,6 @@ _op_real_in_out_name = {
     "sigmoid": [["X"], ["Out"]],
     "elementwise_mul": [["X", "Y"], ["Out"]],
     "elementwise_pow": [["X", "Y"], ["Out"]],
-    "scale": [["X"], ["Out"]],
     "hard_swish": [["X"], ["Out"]],
     "hard_sigmoid": [["X"], ["Out"]],
     "gru": [["Input", "Weight"], ["Hidden"]],
@@ -414,3 +413,26 @@ def calculate_quant_cos_error(orig_tensor, qdq_tensor):
     cos_sim = np.inner(orig_tensor.flatten(), qdq_tensor.flatten()) \
               / (np.linalg.norm(orig_tensor.flatten()) * np.linalg.norm(qdq_tensor.flatten()))
     return cos_sim
+
+
+class tqdm(object):
+    def __init__(self, total, bar_format='Loading|{bar}', ncols=80):
+        self.total = total
+        self.bar_format = bar_format
+        self.ncols = ncols
+        self.n = 0
+
+    def update(self, n=1):
+        self.n += n
+        a = "=" * round((self.n / self.total) * self.ncols)
+        b = " " * (self.ncols - len(a))
+        prefix = self.bar_format.split('|')[0]
+        sys.stderr.write("\r{}|{}=>{}| {}/{}".format(prefix, a, b, self.n,
+                                                     self.total))
+        sys.stderr.flush()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        sys.stderr.write('\n')