[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/quantization_pass.py

@@ -14,6 +14,10 @@
 
 import collections
 import numpy as np
+try:
+    from tqdm import tqdm
+except:
+    from .utils import tqdm
 from ..... import compat as cpt
 from .... import core
 from ....framework import IrGraph
@@ -294,10 +298,10 @@ class QuantizationTransformPass(object):
                         else False
 
                     # if var node is weight and weight_preprocess_func is not None,
-                    # will insert weight preprocess func 
+                    # will insert weight preprocess func
                     # to preorocess weight before quantization
-                    # if var node is activation and act_preprocess_func is not None, 
-                    # will insert activation preprocess func 
+                    # if var node is activation and act_preprocess_func is not None,
+                    # will insert activation preprocess func
                     # to preorocess activation before quantization
                     if is_weight and self._weight_preprocess_func is not None:
                         var_node = self._insert_func(
@@ -372,10 +376,15 @@ class QuantizationTransformPass(object):
         graph.out_node_mapping_table = dict()
         # The process of _transform_forward and _transform_backward is needed in two for loops.
         # The loop for transforming the forward graph:
-        for op in ops:
-            if op.name() in self._quantizable_ops:
-                if not self._is_skip_quant(graph, op) and _has_weight(op):
-                    _transform_forward(graph, op)
+        with tqdm(
+                total=len(ops),
+                bar_format='Adding quant op for weight:|{bar}| {n_fmt}/{total_fmt}',
+                ncols=80) as t:
+            for op in ops:
+                if op.name() in self._quantizable_ops:
+                    if not self._is_skip_quant(graph, op) and _has_weight(op):
+                        _transform_forward(graph, op)
+                t.update()
         # The loop for renaming the inputs of backward op.
         for op in ops:
             if op.name() in self._quantizable_grad_ops and _has_weight(op):
@@ -1427,85 +1436,92 @@ class OutScaleForTrainingPass(object):
         for op in graph.all_op_nodes():
             if op.name() in self._teller_set:
                 target_ops.append(op)
-        for op in target_ops:
-            for output_var_name in utils._get_op_output_var_names(op):
-                in_node = graph._find_node_by_name(op.outputs, output_var_name)
-                if in_node.dtype() not in \
-                    [core.VarDesc.VarType.FP64, core.VarDesc.VarType.FP32]:
-                    continue
+        with tqdm(
+                total=len(target_ops),
+                bar_format='Adding OutScale op:|{bar}| {n_fmt}/{total_fmt}',
+                ncols=80) as t:
+            for op in target_ops:
+                for output_var_name in utils._get_op_output_var_names(op):
+                    in_node = graph._find_node_by_name(op.outputs,
+                                                       output_var_name)
+                    if in_node.dtype() not in \
+                        [core.VarDesc.VarType.FP64, core.VarDesc.VarType.FP32]:
+                        continue
 
-                scale_node = graph.create_persistable_node(
-                    name=self._scale_name(in_node.name()),
-                    var_type=core.VarDesc.VarType.LOD_TENSOR,
-                    shape=[1],
-                    var_dtype=in_node.dtype())
-                data_type = 'float64' if in_node.dtype() \
-                    == core.VarDesc.VarType.FP64 else 'float32'
-                _init_var_node(
-                    scale_node,
-                    np.ones(
-                        [1], dtype=data_type),
-                    self._scope,
-                    self._place)
-                ins = {'X': in_node}
-                outs = {'OutScale': scale_node}
-                if not self._is_test:
-                    state_in_node = graph.create_persistable_node(
-                        name=unique_name.generate('scale_state@'),
+                    scale_node = graph.create_persistable_node(
+                        name=self._scale_name(in_node.name()),
                         var_type=core.VarDesc.VarType.LOD_TENSOR,
-                        var_dtype=in_node.dtype(),
-                        shape=[1])
+                        shape=[1],
+                        var_dtype=in_node.dtype())
+                    data_type = 'float64' if in_node.dtype() \
+                        == core.VarDesc.VarType.FP64 else 'float32'
                     _init_var_node(
-                        state_in_node,
+                        scale_node,
                         np.ones(
                             [1], dtype=data_type),
                         self._scope,
                         self._place)
-                    accum_in_node = graph.create_persistable_node(
-                        name=unique_name.generate('scale_accum@'),
-                        var_type=core.VarDesc.VarType.LOD_TENSOR,
-                        var_dtype=in_node.dtype(),
-                        shape=[1])
-                    _init_var_node(
-                        accum_in_node,
-                        np.ones(
-                            [1], dtype=data_type),
-                        self._scope,
-                        self._place)
-                    state_out_node = graph.create_var_node_from_desc(
-                        state_in_node.var())
-                    accum_out_node = graph.create_var_node_from_desc(
-                        accum_in_node.var())
-
-                    ins['InState'] = state_in_node
-                    ins['InAccum'] = accum_in_node
-                    outs['OutState'] = state_out_node
-                    outs['OutAccum'] = accum_out_node
-
-                attrs = {
-                    'moving_rate': self._moving_rate,
-                    'is_test': self._is_test,
-                    'op_role': core.op_proto_and_checker_maker.OpRole.Forward
-                }
-                scale_op_node = graph.create_op_node(
-                    op_type='moving_average_abs_max_scale',
-                    attrs=attrs,
-                    inputs=ins,
-                    outputs=outs)
-                graph.link_to(in_node, scale_op_node)
-                graph.link_to(scale_op_node, scale_node)
-                if not self._is_test:
-                    graph.link_to(state_in_node, scale_op_node)
-                    graph.link_to(accum_in_node, scale_op_node)
-                    graph.link_to(scale_op_node, state_out_node)
-                    graph.link_to(scale_op_node, accum_out_node)
+                    ins = {'X': in_node}
+                    outs = {'OutScale': scale_node}
+                    if not self._is_test:
+                        state_in_node = graph.create_persistable_node(
+                            name=unique_name.generate('scale_state@'),
+                            var_type=core.VarDesc.VarType.LOD_TENSOR,
+                            var_dtype=in_node.dtype(),
+                            shape=[1])
+                        _init_var_node(
+                            state_in_node,
+                            np.ones(
+                                [1], dtype=data_type),
+                            self._scope,
+                            self._place)
+                        accum_in_node = graph.create_persistable_node(
+                            name=unique_name.generate('scale_accum@'),
+                            var_type=core.VarDesc.VarType.LOD_TENSOR,
+                            var_dtype=in_node.dtype(),
+                            shape=[1])
+                        _init_var_node(
+                            accum_in_node,
+                            np.ones(
+                                [1], dtype=data_type),
+                            self._scope,
+                            self._place)
+                        state_out_node = graph.create_var_node_from_desc(
+                            state_in_node.var())
+                        accum_out_node = graph.create_var_node_from_desc(
+                            accum_in_node.var())
+
+                        ins['InState'] = state_in_node
+                        ins['InAccum'] = accum_in_node
+                        outs['OutState'] = state_out_node
+                        outs['OutAccum'] = accum_out_node
+
+                    attrs = {
+                        'moving_rate': self._moving_rate,
+                        'is_test': self._is_test,
+                        'op_role':
+                        core.op_proto_and_checker_maker.OpRole.Forward
+                    }
+                    scale_op_node = graph.create_op_node(
+                        op_type='moving_average_abs_max_scale',
+                        attrs=attrs,
+                        inputs=ins,
+                        outputs=outs)
+                    graph.link_to(in_node, scale_op_node)
+                    graph.link_to(scale_op_node, scale_node)
+                    if not self._is_test:
+                        graph.link_to(state_in_node, scale_op_node)
+                        graph.link_to(accum_in_node, scale_op_node)
+                        graph.link_to(scale_op_node, state_out_node)
+                        graph.link_to(scale_op_node, accum_out_node)
+                t.update()
         return graph
 
     def _scale_name(self, var_name):
         """
         Return the scale name for the var named `var_name`.
         """
-        return "%s.scale" % (var_name)
+        return "%s@scale" % (var_name)
 
 
 class OutScaleForInferencePass(object):
@@ -1564,7 +1580,7 @@ class OutScaleForInferencePass(object):
         """
         Return the scale name for the var named `var_name`.
         """
-        return "%s.scale" % (var_name)
+        return "%s@scale" % (var_name)
 
 
 class AddQuantDequantPass(object):
@@ -1644,36 +1660,43 @@ class AddQuantDequantPass(object):
 
         # Forward stage, insert quant_dequant op
         all_op_nodes = graph.all_op_nodes()
-        for op_node in all_op_nodes:
-            if op_node.name() in self._quantizable_op_type:
-                is_skip = False
-                if isinstance(self._skip_pattern, list):
-                    is_skip = op_node.op().has_attr("op_namescope") and \
-                                   any(pattern in op_node.op().attr("op_namescope") for pattern in self._skip_pattern)
-                elif isinstance(self._skip_pattern, str):
-                    is_skip = op_node.op().has_attr("op_namescope") and \
-                                   op_node.op().attr("op_namescope").find(self._skip_pattern) != -1
-                is_quantized = op_node.op().has_attr("quantization_type") and \
-                    op_node.op().attr("quantization_type") == "qat_with_weight"
-                if is_skip or is_quantized or \
-                    (not _is_input_all_not_persistable(graph, op_node)):
-                    continue
+        with tqdm(
+                total=len(all_op_nodes),
+                bar_format='Adding quant activation op:|{bar}| {n_fmt}/{total_fmt}',
+                ncols=80) as t:
+            for op_node in all_op_nodes:
+                if op_node.name() in self._quantizable_op_type:
+                    is_skip = False
+                    if isinstance(self._skip_pattern, list):
+                        is_skip = op_node.op().has_attr("op_namescope") and \
+                                    any(pattern in op_node.op().attr("op_namescope") for pattern in self._skip_pattern)
+                    elif isinstance(self._skip_pattern, str):
+                        is_skip = op_node.op().has_attr("op_namescope") and \
+                                    op_node.op().attr("op_namescope").find(self._skip_pattern) != -1
+                    is_quantized = op_node.op().has_attr("quantization_type") and \
+                        op_node.op().attr("quantization_type") == "qat_with_weight"
+                    if is_skip or is_quantized or \
+                        (not _is_input_all_not_persistable(graph, op_node)):
+                        continue
 
-                op_node.op()._set_attr("quantization_type",
-                                       "qat_without_weight")
-                op_node.op()._set_attr("activation_bits", self._quant_bits)
-                op_node.op()._set_attr("with_quant_attr", True)
-                arg_names = utils._get_op_input_var_names(op_node)
-                for arg_name in arg_names:
-                    in_node = graph._find_node_by_name(op_node.inputs, arg_name)
-                    if arg_name in dequantized_vars_map:
-                        quant_var_node = dequantized_vars_map[arg_name]
-                    else:
-                        quant_var_node, _ = \
-                            self._inser_quant_dequant_moving_average_abs_max_op(
-                            graph, in_node, self._quant_bits)
-                        dequantized_vars_map[arg_name] = quant_var_node
-                    graph.update_input_link(in_node, quant_var_node, op_node)
+                    op_node.op()._set_attr("quantization_type",
+                                           "qat_without_weight")
+                    op_node.op()._set_attr("activation_bits", self._quant_bits)
+                    op_node.op()._set_attr("with_quant_attr", True)
+                    arg_names = utils._get_op_input_var_names(op_node)
+                    for arg_name in arg_names:
+                        in_node = graph._find_node_by_name(op_node.inputs,
+                                                           arg_name)
+                        if arg_name in dequantized_vars_map:
+                            quant_var_node = dequantized_vars_map[arg_name]
+                        else:
+                            quant_var_node, _ = \
+                                self._inser_quant_dequant_moving_average_abs_max_op(
+                                graph, in_node, self._quant_bits)
+                            dequantized_vars_map[arg_name] = quant_var_node
+                        graph.update_input_link(in_node, quant_var_node,
+                                                op_node)
+            t.update()
 
         # Backward stage, update input link
         for op_node in all_op_nodes:
@@ -2122,10 +2145,10 @@ class QuantizationTransformPassV2(object):
                     else False
 
                 # if var node is weight and weight_preprocess_func is not None,
-                # will insert weight preprocess func 
+                # will insert weight preprocess func
                 # to preorocess weight before quantization
-                # if var node is activation and act_preprocess_func is not None, 
-                # will insert activation preprocess func 
+                # if var node is activation and act_preprocess_func is not None,
+                # will insert activation preprocess func
                 # to preorocess activation before quantization
                 if is_weight and self._weight_preprocess_func is not None:
                     var_node = self._insert_func(
@@ -2240,10 +2263,16 @@ class QuantizationTransformPassV2(object):
         graph.out_node_mapping_table = dict()
         # The process of _transform_forward and _transform_backward is needed in two for loops.
         # The loop for transforming the forward graph:
-        for op in ops:
-            if op.name() in self._quantizable_ops:
-                if not self._is_skip_quant(graph, op) and self._has_weight(op):
-                    self._transform_forward(graph, op)
+        with tqdm(
+                total=len(ops),
+                bar_format='Adding quant op for weight:|{bar}| {n_fmt}/{total_fmt}',
+                ncols=80) as t:
+            for op in ops:
+                if op.name() in self._quantizable_ops:
+                    if not self._is_skip_quant(graph,
+                                               op) and self._has_weight(op):
+                        self._transform_forward(graph, op)
+                t.update()
         # The loop for renaming the inputs of backward op.
         for op in ops:
             if op.name() in self._quantizable_grad_ops and self._has_weight(op):
@@ -2346,43 +2375,50 @@ class AddQuantDequantPassV2(object):
 
         # Forward stage, insert quant_dequant op
         all_op_nodes = graph.all_op_nodes()
-        for op_node in all_op_nodes:
-            if op_node.name() in self._quantizable_op_type:
-                is_skip = False
-                if isinstance(self._skip_pattern, list):
-                    is_skip = op_node.op().has_attr("op_namescope") and \
-                                   any(pattern in op_node.op().attr("op_namescope") for pattern in self._skip_pattern)
-                elif isinstance(self._skip_pattern, str):
-                    is_skip = op_node.op().has_attr("op_namescope") and \
-                                   op_node.op().attr("op_namescope").find(self._skip_pattern) != -1
-                is_quantized = op_node.op().has_attr("quantization_type") and \
-                    op_node.op().attr("quantization_type") == "qat_with_weight"
-                if is_skip or is_quantized:
-                    continue
-
-                op_node.op()._set_attr("quantization_type",
-                                       "qat_without_weight")
-                arg_names = utils._get_op_input_var_names(op_node)
-                for arg_name in arg_names:
-                    in_node = graph._find_node_by_name(op_node.inputs, arg_name)
-                    if in_node.persistable():
+        with tqdm(
+                total=len(all_op_nodes),
+                bar_format='Adding quant activation op:|{bar}| {n_fmt}/{total_fmt}',
+                ncols=80) as t:
+            for op_node in all_op_nodes:
+                if op_node.name() in self._quantizable_op_type:
+                    is_skip = False
+                    if isinstance(self._skip_pattern, list):
+                        is_skip = op_node.op().has_attr("op_namescope") and \
+                                    any(pattern in op_node.op().attr("op_namescope") for pattern in self._skip_pattern)
+                    elif isinstance(self._skip_pattern, str):
+                        is_skip = op_node.op().has_attr("op_namescope") and \
+                                    op_node.op().attr("op_namescope").find(self._skip_pattern) != -1
+                    is_quantized = op_node.op().has_attr("quantization_type") and \
+                        op_node.op().attr("quantization_type") == "qat_with_weight"
+                    if is_skip or is_quantized:
                         continue
-                    if arg_name in dequantized_vars_map:
-                        dequant_var_node = dequantized_vars_map[arg_name]
-                    else:
-                        insert_quant_pass = InsertQuantizeLinear(
-                            self._place,
-                            self._scope,
-                            quant_bits=self._quant_bits,
-                            quant_axis=-1,
-                            channel_wise=False,
-                            is_test=self._is_test)
-                        quant_var_node, scale_var_node = insert_quant_pass.insert_quant_op(
-                            graph, in_node)
-                        dequant_var_node = insert_quant_pass.insert_dequant_op(
-                            graph, quant_var_node, scale_var_node)
-                        dequantized_vars_map[arg_name] = dequant_var_node
-                    graph.update_input_link(in_node, dequant_var_node, op_node)
+
+                    op_node.op()._set_attr("quantization_type",
+                                           "qat_without_weight")
+                    arg_names = utils._get_op_input_var_names(op_node)
+                    for arg_name in arg_names:
+                        in_node = graph._find_node_by_name(op_node.inputs,
+                                                           arg_name)
+                        if in_node.persistable():
+                            continue
+                        if arg_name in dequantized_vars_map:
+                            dequant_var_node = dequantized_vars_map[arg_name]
+                        else:
+                            insert_quant_pass = InsertQuantizeLinear(
+                                self._place,
+                                self._scope,
+                                quant_bits=self._quant_bits,
+                                quant_axis=-1,
+                                channel_wise=False,
+                                is_test=self._is_test)
+                            quant_var_node, scale_var_node = insert_quant_pass.insert_quant_op(
+                                graph, in_node)
+                            dequant_var_node = insert_quant_pass.insert_dequant_op(
+                                graph, quant_var_node, scale_var_node)
+                            dequantized_vars_map[arg_name] = dequant_var_node
+                        graph.update_input_link(in_node, dequant_var_node,
+                                                op_node)
+                t.update()
 
         # Backward stage, update input link
         for op_node in all_op_nodes:

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')