Merge pull request #208 from microsoft/master

merge master

examples/trials/systems/rocksdb-fillrandom/config_tpe.yml

+authorName: default
+experimentName: auto_rocksdb_TPE
+trialConcurrency: 1
+maxExecDuration: 12h
+maxTrialNum: 256
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
+  #SMAC (SMAC should be installed through nnictl)
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python3 main.py
+  codeDir: .
+  gpuNum: 0

examples/trials/systems/rocksdb-fillrandom/db_bench_installation.sh

+#!/bin/bash
+# Install db_bench and its dependencies on Ubuntu
+
+pushd $PWD 1>/dev/null
+
+# install snappy
+echo "****************** Installing snappy *******************"
+sudo apt-get install libsnappy-dev -y
+
+# install gflag
+echo "****************** Installing gflag ********************"
+cd /tmp
+git clone https://github.com/gflags/gflags.git
+cd gflags
+git checkout v2.0
+./configure && make && sudo make install
+
+# install rocksdb
+echo "****************** Installing rocksdb ******************"
+cd /tmp
+git clone https://github.com/facebook/rocksdb.git
+cd rocksdb
+CPATH=/usr/local/include LIBRARY_PATH=/usr/local/lib DEBUG_LEVEL=0 make db_bench -j7
+
+DIR=$HOME/.local/bin/
+if [[ ! -e $DIR ]]; then
+    mkdir $dir
+elif [[ ! -d $DIR ]]; then
+    echo "$DIR already exists but is not a directory" 1>&2
+    exit
+fi
+mv db_bench $HOME/.local/bin &&
+echo "Successfully installed rocksed in "$DIR" !" &&
+echo "Please add "$DIR" to your PATH for runing this example."
+
+popd 1>/dev/null

examples/trials/systems/rocksdb-fillrandom/main.py

+# Copyright (c) Microsoft Corporation
+# All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction, including without limitation
+# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and
+# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
+# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import nni
+import subprocess
+import logging
+
+LOG = logging.getLogger('rocksdb-fillrandom')
+
+
+def run(**parameters):
+    '''Run rocksdb benchmark and return throughput'''
+    bench_type = parameters['benchmarks']
+    # recover args
+    args = ["--{}={}".format(k, v) for k, v in parameters.items()]
+    # subprocess communicate
+    process = subprocess.Popen(['db_bench'] + args, stdout=subprocess.PIPE)
+    out, err = process.communicate()
+    # split into lines
+    lines = out.decode("utf8").splitlines()
+
+    match_lines = []
+    for line in lines:
+        # find the line with matched str
+        if bench_type not in line:
+            continue
+        else:
+            match_lines.append(line)
+            break
+
+    results = {}
+    for line in match_lines:
+        key, _, value = line.partition(":")
+        key = key.strip()
+        value = value.split("op")[1]
+        results[key] = float(value)
+
+    return results[bench_type]
+
+
+def generate_params(received_params):
+    '''generate parameters based on received parameters'''
+    params = {
+        "benchmarks": "fillrandom",
+        "threads": 1,
+        "key_size": 20,
+        "value_size": 100,
+        "num": 13107200,
+        "db": "/tmp/rockdb",
+        "disable_wal": 1,
+        "max_background_flushes": 1,
+        "max_background_compactions": 4,
+        "write_buffer_size": 67108864,
+        "max_write_buffer_number": 16,
+        "min_write_buffer_number_to_merge": 2,
+        "level0_file_num_compaction_trigger": 2,
+        "max_bytes_for_level_base": 268435456,
+        "max_bytes_for_level_multiplier": 10,
+        "target_file_size_base": 33554432,
+        "target_file_size_multiplier": 1
+    }
+
+    for k, v in received_params.items():
+        params[k] = int(v)
+
+    return params
+
+
+if __name__ == "__main__":
+    try:
+        # get parameters from tuner
+        RECEIVED_PARAMS = nni.get_next_parameter()
+        LOG.debug(RECEIVED_PARAMS)
+        PARAMS = generate_params(RECEIVED_PARAMS)
+        LOG.debug(PARAMS)
+        # run benchmark
+        throughput = run(**PARAMS)
+        # report throughput to nni
+        nni.report_final_result(throughput)
+    except Exception as exception:
+        LOG.exception(exception)
+        raise

examples/trials/systems/rocksdb-fillrandom/search_space.json

+{
+    "write_buffer_size": {
+        "_type": "quniform",
+        "_value": [2097152, 16777216, 1048576]
+    },
+    "min_write_buffer_number_to_merge": {
+        "_type": "quniform",
+        "_value": [2, 16, 1]
+    },
+    "level0_file_num_compaction_trigger": {
+        "_type": "quniform",
+        "_value": [2, 16, 1]
+    }
+}

setup.py

@@ -48,7 +48,7 @@ setup(
     python_requires = '>=3.5',
     install_requires = [
         'astor',
-        'hyperopt',
+        'hyperopt==0.1.2',
         'json_tricks',
         'numpy',
         'psutil',

src/sdk/pynni/nni/compression/tensorflow/builtin_pruners.py

@@ -2,7 +2,7 @@ import logging
 import tensorflow as tf
 from .compressor import Pruner
 
-__all__ = [ 'LevelPruner', 'AGP_Pruner', 'SensitivityPruner' ]
+__all__ = ['LevelPruner', 'AGP_Pruner']
 
 _logger = logging.getLogger(__name__)
 
@@ -14,10 +14,18 @@ class LevelPruner(Pruner):
             - sparsity
         """
         super().__init__(config_list)
+        self.mask_list = {}
+        self.if_init_list = {}
 
-    def calc_mask(self, weight, config, **kwargs):
+    def calc_mask(self, weight, config, op_name, **kwargs):
+        if self.if_init_list.get(op_name, True):
             threshold = tf.contrib.distributions.percentile(tf.abs(weight), config['sparsity'] * 100)
-        return tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype)
+            mask = tf.cast(tf.math.greater(tf.abs(weight), threshold), weight.dtype)
+            self.mask_list.update({op_name: mask})
+            self.if_init_list.update({op_name: False})
+        else:
+            mask = self.mask_list[op_name]
+        return mask
 
 
 class AGP_Pruner(Pruner):
@@ -29,6 +37,7 @@ class AGP_Pruner(Pruner):
     Learning of Phones and other Consumer Devices,
     https://arxiv.org/pdf/1710.01878.pdf
     """
+
     def __init__(self, config_list):
         """
         config_list: supported keys:
@@ -39,15 +48,25 @@ class AGP_Pruner(Pruner):
             - frequency: if you want update every 2 epoch, you can set it 2
         """
         super().__init__(config_list)
+        self.mask_list = {}
+        self.if_init_list = {}
         self.now_epoch = tf.Variable(0)
         self.assign_handler = []
 
-    def calc_mask(self, weight, config, **kwargs):
+    def calc_mask(self, weight, config, op_name, **kwargs):
+        start_epoch = config.get('start_epoch', 0)
+        freq = config.get('frequency', 1)
+        if self.now_epoch >= start_epoch and self.if_init_list.get(op_name, True) and (
+                self.now_epoch - start_epoch) % freq == 0:
             target_sparsity = self.compute_target_sparsity(config)
             threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100)
             # stop gradient in case gradient change the mask
             mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype))
             self.assign_handler.append(tf.assign(weight, weight * mask))
+            self.mask_list.update({op_name: tf.constant(mask)})
+            self.if_init_list.update({op_name: False})
+        else:
+            mask = self.mask_list[op_name]
         return mask
 
     def compute_target_sparsity(self, config):
@@ -62,49 +81,16 @@ class AGP_Pruner(Pruner):
             return final_sparsity
 
         now_epoch = tf.minimum(self.now_epoch, tf.constant(end_epoch))
-        span = int(((end_epoch - start_epoch-1)//freq)*freq)
+        span = int(((end_epoch - start_epoch - 1) // freq) * freq)
         assert span > 0
         base = tf.cast(now_epoch - start_epoch, tf.float32) / span
         target_sparsity = (final_sparsity +
-                            (initial_sparsity - final_sparsity)*
+                           (initial_sparsity - final_sparsity) *
                            (tf.pow(1.0 - base, 3)))
         return target_sparsity
 
     def update_epoch(self, epoch, sess):
         sess.run(self.assign_handler)
         sess.run(tf.assign(self.now_epoch, int(epoch)))
-    
-
-class SensitivityPruner(Pruner):
-    """Use algorithm from "Learning both Weights and Connections for Efficient Neural Networks" 
-    https://arxiv.org/pdf/1506.02626v3.pdf
-
-    I.e.: "The pruning threshold is chosen as a quality parameter multiplied
-    by the standard deviation of a layers weights."
-    """
-    def __init__(self, config_list):
-        """
-        config_list: supported keys
-            - sparsity: chosen pruning sparsity
-        """
-        super().__init__(config_list)
-        self.layer_mask = {}
-        self.assign_handler = []
-
-    def calc_mask(self, weight, config, op_name, **kwargs):
-        target_sparsity = config['sparsity'] * tf.math.reduce_std(weight) 
-        mask = tf.get_variable(op_name + '_mask', initializer=tf.ones(weight.shape), trainable=False)
-        self.layer_mask[op_name] = mask
-
-        weight_assign_handler = tf.assign(weight, mask*weight)
-        # use control_dependencies so that weight_assign_handler will be executed before mask_update_handler
-        with tf.control_dependencies([weight_assign_handler]):
-            threshold = tf.contrib.distributions.percentile(weight, target_sparsity * 100)
-            # stop gradient in case gradient change the mask
-            new_mask = tf.stop_gradient(tf.cast(tf.math.greater(weight, threshold), weight.dtype))
-            mask_update_handler = tf.assign(mask, new_mask)
-            self.assign_handler.append(mask_update_handler)
-        return mask
-
-    def update_epoch(self, epoch, sess):
-        sess.run(self.assign_handler)
+        for k in self.if_init_list.keys():
+            self.if_init_list[k] = True

src/sdk/pynni/nni/compression/torch/builtin_pruners.py

@@ -2,7 +2,7 @@ import logging
 import torch
 from .compressor import Pruner
 
-__all__ = [ 'LevelPruner', 'AGP_Pruner', 'SensitivityPruner' ]
+__all__ = ['LevelPruner', 'AGP_Pruner']
 
 logger = logging.getLogger('torch pruner')
 
@@ -10,20 +10,29 @@ logger = logging.getLogger('torch pruner')
 class LevelPruner(Pruner):
     """Prune to an exact pruning level specification
     """
+
     def __init__(self, config_list):
         """
         config_list: supported keys:
             - sparsity
         """
         super().__init__(config_list)
+        self.mask_list = {}
+        self.if_init_list = {}
 
-    def calc_mask(self, weight, config, **kwargs):
+    def calc_mask(self, weight, config, op_name, **kwargs):
+        if self.if_init_list.get(op_name, True):
             w_abs = weight.abs()
             k = int(weight.numel() * config['sparsity'])
             if k == 0:
-            return torch.ones(weight.shape)
-        threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max()
-        return torch.gt(w_abs, threshold).type(weight.type())
+                return torch.ones(weight.shape).type_as(weight)
+            threshold = torch.topk(w_abs.view(-1), k, largest=False).values.max()
+            mask = torch.gt(w_abs, threshold).type_as(weight)
+            self.mask_list.update({op_name: mask})
+            self.if_init_list.update({op_name: False})
+        else:
+            mask = self.mask_list[op_name]
+        return mask
 
 
 class AGP_Pruner(Pruner):
@@ -35,35 +44,44 @@ class AGP_Pruner(Pruner):
     Learning of Phones and other Consumer Devices,
     https://arxiv.org/pdf/1710.01878.pdf
     """
+
     def __init__(self, config_list):
         """
         config_list: supported keys:
             - initial_sparsity
             - final_sparsity: you should make sure initial_sparsity <= final_sparsity
-            - start_epoch: start epoch numer begin update mask
+            - start_epoch: start epoch number begin update mask
             - end_epoch: end epoch number stop update mask, you should make sure start_epoch <= end_epoch
             - frequency: if you want update every 2 epoch, you can set it 2
         """
         super().__init__(config_list)
         self.mask_list = {}
-        self.now_epoch = 1
+        self.now_epoch = 0
+        self.if_init_list = {}
 
     def calc_mask(self, weight, config, op_name, **kwargs):
-        mask = self.mask_list.get(op_name, torch.ones(weight.shape))
+        start_epoch = config.get('start_epoch', 0)
+        freq = config.get('frequency', 1)
+        if self.now_epoch >= start_epoch and self.if_init_list.get(op_name, True) and (
+                self.now_epoch - start_epoch) % freq == 0:
+            mask = self.mask_list.get(op_name, torch.ones(weight.shape).type_as(weight))
             target_sparsity = self.compute_target_sparsity(config)
             k = int(weight.numel() * target_sparsity)
             if k == 0 or target_sparsity >= 1 or target_sparsity <= 0:
                 return mask
             # if we want to generate new mask, we should update weigth first
-        w_abs = weight.abs()*mask
-        threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max()
-        new_mask = torch.gt(w_abs, threshold).type(weight.type())
-        self.mask_list[op_name] = new_mask
+            w_abs = weight.abs() * mask
+            threshold = torch.topk(w_abs.view(-1), k, largest=False).values.max()
+            new_mask = torch.gt(w_abs, threshold).type_as(weight)
+            self.mask_list.update({op_name: new_mask})
+            self.if_init_list.update({op_name: False})
+        else:
+            new_mask = self.mask_list.get(op_name, torch.ones(weight.shape).type_as(weight))
         return new_mask
 
     def compute_target_sparsity(self, config):
         end_epoch = config.get('end_epoch', 1)
-        start_epoch = config.get('start_epoch', 1)
+        start_epoch = config.get('start_epoch', 0)
         freq = config.get('frequency', 1)
         final_sparsity = config.get('final_sparsity', 0)
         initial_sparsity = config.get('initial_sparsity', 0)
@@ -74,45 +92,15 @@ class AGP_Pruner(Pruner):
         if end_epoch <= self.now_epoch:
             return final_sparsity
 
-        span = ((end_epoch - start_epoch-1)//freq)*freq
+        span = ((end_epoch - start_epoch - 1) // freq) * freq
         assert span > 0
         target_sparsity = (final_sparsity +
-                            (initial_sparsity - final_sparsity)*
-                            (1.0 - ((self.now_epoch - start_epoch)/span))**3)
+                           (initial_sparsity - final_sparsity) *
+                           (1.0 - ((self.now_epoch - start_epoch) / span)) ** 3)
         return target_sparsity
 
     def update_epoch(self, epoch):
         if epoch > 0:
             self.now_epoch = epoch
-    
-    
-class SensitivityPruner(Pruner):
-    """Use algorithm from "Learning both Weights and Connections for Efficient Neural Networks" 
-    https://arxiv.org/pdf/1506.02626v3.pdf
-
-    I.e.: "The pruning threshold is chosen as a quality parameter multiplied
-    by the standard deviation of a layers weights."
-    """
-    def __init__(self, config_list):
-        """
-        config_list: supported keys:
-            - sparsity: chosen pruning sparsity
-        """
-        super().__init__(config_list)
-        self.mask_list = {}
-    
-   
-    def calc_mask(self, weight, config, op_name, **kwargs):
-        mask = self.mask_list.get(op_name, torch.ones(weight.shape))
-        # if we want to generate new mask, we should update weigth first 
-        weight = weight*mask
-        target_sparsity = config['sparsity'] * torch.std(weight).item()
-        k = int(weight.numel() * target_sparsity)
-        if k == 0:
-            return mask
-        
-        w_abs = weight.abs()
-        threshold = torch.topk(w_abs.view(-1), k, largest = False).values.max()
-        new_mask = torch.gt(w_abs, threshold).type(weight.type())
-        self.mask_list[op_name] = new_mask
-        return new_mask
+            for k in self.if_init_list.keys():
+                self.if_init_list[k] = True

src/sdk/pynni/nni/compression/torch/compressor.py

@@ -38,7 +38,6 @@ class Compressor:
             if config is not None:
                 self._instrument_layer(layer, config)
 
-
     def bind_model(self, model):
         """This method is called when a model is bound to the compressor.
         Users can optionally overload this method to do model-specific initialization.
@@ -56,7 +55,6 @@ class Compressor:
         """
         pass
 
-
     def _instrument_layer(self, layer, config):
         raise NotImplementedError()
 
@@ -90,7 +88,6 @@ class Pruner(Compressor):
         """
         raise NotImplementedError("Pruners must overload calc_mask()")
 
-
     def _instrument_layer(self, layer, config):
         # TODO: support multiple weight tensors
         # create a wrapper forward function to replace the original one

src/sdk/pynni/requirements.txt

@@ -4,7 +4,7 @@ json_tricks
 # hyperopt tuner
 numpy
 scipy
-hyperopt
+hyperopt==0.1.2
 
 # metis tuner
 sklearn

src/sdk/pynni/setup.py

@@ -32,7 +32,7 @@ setuptools.setup(
 
     python_requires = '>=3.5',
     install_requires = [
-        'hyperopt',
+        'hyperopt==0.1.2',
         'json_tricks',
         'numpy',
         'scipy',

src/sdk/pynni/tests/test_compressor.py

@@ -5,24 +5,28 @@ import torch.nn.functional as F
 import nni.compression.tensorflow as tf_compressor
 import nni.compression.torch as torch_compressor
 
+
 def weight_variable(shape):
-    return tf.Variable(tf.truncated_normal(shape, stddev = 0.1))
+    return tf.Variable(tf.truncated_normal(shape, stddev=0.1))
+
 
 def bias_variable(shape):
-    return tf.Variable(tf.constant(0.1, shape = shape))
+    return tf.Variable(tf.constant(0.1, shape=shape))
+
 
 def conv2d(x_input, w_matrix):
-    return tf.nn.conv2d(x_input, w_matrix, strides = [ 1, 1, 1, 1 ], padding = 'SAME')
+    return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME')
+
 
 def max_pool(x_input, pool_size):
-    size = [ 1, pool_size, pool_size, 1 ]
-    return tf.nn.max_pool(x_input, ksize = size, strides = size, padding = 'SAME')
+    size = [1, pool_size, pool_size, 1]
+    return tf.nn.max_pool(x_input, ksize=size, strides=size, padding='SAME')
 
 
 class TfMnist:
     def __init__(self):
-        images = tf.placeholder(tf.float32, [ None, 784 ], name = 'input_x')
-        labels = tf.placeholder(tf.float32, [ None, 10 ], name = 'input_y')
+        images = tf.placeholder(tf.float32, [None, 784], name='input_x')
+        labels = tf.placeholder(tf.float32, [None, 10], name='input_y')
         keep_prob = tf.placeholder(tf.float32, name='keep_prob')
 
         self.images = images
@@ -37,35 +41,35 @@ class TfMnist:
         self.fcw1 = None
         self.cross = None
         with tf.name_scope('reshape'):
-            x_image = tf.reshape(images, [ -1, 28, 28, 1 ])
+            x_image = tf.reshape(images, [-1, 28, 28, 1])
         with tf.name_scope('conv1'):
-            w_conv1 = weight_variable([ 5, 5, 1, 32 ])
+            w_conv1 = weight_variable([5, 5, 1, 32])
             self.w1 = w_conv1
-            b_conv1 = bias_variable([ 32 ])
+            b_conv1 = bias_variable([32])
             self.b1 = b_conv1
             h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
         with tf.name_scope('pool1'):
             h_pool1 = max_pool(h_conv1, 2)
         with tf.name_scope('conv2'):
-            w_conv2 = weight_variable([ 5, 5, 32, 64 ])
-            b_conv2 = bias_variable([ 64 ])
+            w_conv2 = weight_variable([5, 5, 32, 64])
+            b_conv2 = bias_variable([64])
             h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
         with tf.name_scope('pool2'):
             h_pool2 = max_pool(h_conv2, 2)
         with tf.name_scope('fc1'):
-            w_fc1 = weight_variable([ 7 * 7 * 64, 1024 ])
+            w_fc1 = weight_variable([7 * 7 * 64, 1024])
             self.fcw1 = w_fc1
-            b_fc1 = bias_variable([ 1024 ])
-        h_pool2_flat = tf.reshape(h_pool2, [ -1, 7 * 7 * 64 ])
+            b_fc1 = bias_variable([1024])
+        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
         h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
         with tf.name_scope('dropout'):
             h_fc1_drop = tf.nn.dropout(h_fc1, 0.5)
         with tf.name_scope('fc2'):
-            w_fc2 = weight_variable([ 1024, 10 ])
-            b_fc2 = bias_variable([ 10 ])
+            w_fc2 = weight_variable([1024, 10])
+            b_fc2 = bias_variable([10])
             y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2
         with tf.name_scope('loss'):
-            cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = labels, logits = y_conv))
+            cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=y_conv))
             self.cross = cross_entropy
         with tf.name_scope('adam_optimizer'):
             self.train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy)
@@ -73,6 +77,7 @@ class TfMnist:
             correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(labels, 1))
             self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
+
 class TorchMnist(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -89,22 +94,22 @@ class TorchMnist(torch.nn.Module):
         x = x.view(-1, 4 * 4 * 50)
         x = F.relu(self.fc1(x))
         x = self.fc2(x)
-        return F.log_softmax(x, dim = 1)
+        return F.log_softmax(x, dim=1)
+
 
 class CompressorTestCase(TestCase):
     def test_tf_pruner(self):
         model = TfMnist()
-        configure_list = [{'sparsity':0.8, 'op_types':'default'}]
+        configure_list = [{'sparsity': 0.8, 'op_types': 'default'}]
         tf_compressor.LevelPruner(configure_list).compress_default_graph()
 
-
     def test_tf_quantizer(self):
         model = TfMnist()
         tf_compressor.NaiveQuantizer([{'op_types': 'default'}]).compress_default_graph()
 
     def test_torch_pruner(self):
         model = TorchMnist()
-        configure_list = [{'sparsity':0.8, 'op_types':'default'}]
+        configure_list = [{'sparsity': 0.8, 'op_types': 'default'}]
         torch_compressor.LevelPruner(configure_list).compress(model)
 
     def test_torch_quantizer(self):

src/webui/src/components/Modal/Compare.tsx

@@ -125,6 +125,11 @@ class Compare extends React.Component<CompareProps, {}> {
             durationList.push(temp.duration);
             parameterList.push(temp.description.parameters);
         });
+        let isComplexSearchSpace;
+        if (parameterList.length > 0) {
+            isComplexSearchSpace = (typeof parameterList[0][parameterKeys[0]] === 'object')
+                ? true : false;
+        }
         return (
             <table className="compare">
                 <tbody>
@@ -164,6 +169,10 @@ class Compare extends React.Component<CompareProps, {}> {
                         })}
                     </tr>
                     {
+                        isComplexSearchSpace
+                            ?
+                            null
+                            :
                             Object.keys(parameterKeys).map(index => {
                                 return (
                                     <tr key={index}>

src/webui/src/components/Modal/ExperimentDrawer.tsx

@@ -14,6 +14,7 @@ interface ExpDrawerProps {
 
 interface ExpDrawerState {
     experiment: string;
+    expDrawerHeight: number;
 }
 
 class ExperimentDrawer extends React.Component<ExpDrawerProps, ExpDrawerState> {
@@ -23,7 +24,8 @@ class ExperimentDrawer extends React.Component<ExpDrawerProps, ExpDrawerState> {
         super(props);
 
         this.state = {
-            experiment: ''
+            experiment: '',
+            expDrawerHeight: window.innerHeight - 48
         };
     }
 
@@ -69,9 +71,14 @@ class ExperimentDrawer extends React.Component<ExpDrawerProps, ExpDrawerState> {
         downFile(experiment, 'experiment.json');
     }
 
+    onWindowResize = () => {
+        this.setState(() => ({expDrawerHeight: window.innerHeight - 48}));
+    }
+
     componentDidMount() {
         this._isCompareMount = true;
         this.getExperimentContent();
+        window.addEventListener('resize', this.onWindowResize);
     }
 
     componentWillReceiveProps(nextProps: ExpDrawerProps) {
@@ -83,12 +90,12 @@ class ExperimentDrawer extends React.Component<ExpDrawerProps, ExpDrawerState> {
 
     componentWillUnmount() {
         this._isCompareMount = false;
+        window.removeEventListener('resize', this.onWindowResize);
     }
 
     render() {
         const { isVisble, closeExpDrawer } = this.props;
-        const { experiment } = this.state;
-        const heights: number = window.innerHeight - 48;
+        const { experiment, expDrawerHeight } = this.state;
         return (
             <Row className="logDrawer">
                 <Drawer
@@ -99,15 +106,16 @@ class ExperimentDrawer extends React.Component<ExpDrawerProps, ExpDrawerState> {
                     onClose={closeExpDrawer}
                     visible={isVisble}
                     width="54%"
-                    height={heights}
+                    height={expDrawerHeight}
                 >
-                    <div className="card-container log-tab-body" style={{ height: heights }}>
-                        <Tabs type="card" style={{ height: heights + 19 }}>
+                    {/* 104: tabHeight(40) + tabMarginBottom(16) + buttonHeight(32) + buttonMarginTop(16) */}
+                    <div className="card-container log-tab-body">
+                        <Tabs type="card" style={{ height: expDrawerHeight, minHeight: 190 }}>
                             <TabPane tab="Experiment Parameters" key="Experiment">
                                 <div className="just-for-log">
                                     <MonacoEditor
                                         width="100%"
-                                        height={heights * 0.9}
+                                        height={expDrawerHeight - 104}
                                         language="json"
                                         value={experiment}
                                         options={DRAWEROPTION}

src/webui/src/components/Modal/LogDrawer.tsx

@@ -8,89 +8,48 @@ import MonacoHTML from '../public-child/MonacoEditor';
 import '../../static/style/logDrawer.scss';
 
 interface LogDrawerProps {
-    isVisble: boolean;
     closeDrawer: () => void;
     activeTab?: string;
 }
 
 interface LogDrawerState {
-    nniManagerLogStr: string;
-    dispatcherLogStr: string;
+    nniManagerLogStr: string | null;
+    dispatcherLogStr: string | null;
     isLoading: boolean;
-    isLoadispatcher: boolean;
+    logDrawerHeight: number;
 }
 
 class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
+    private timerId: number | undefined;
 
-    public _isLogDrawer: boolean;
     constructor(props: LogDrawerProps) {
         super(props);
 
         this.state = {
-            nniManagerLogStr: 'nnimanager',
-            dispatcherLogStr: 'dispatcher',
-            isLoading: false,
-            isLoadispatcher: false
+            nniManagerLogStr: null,
+            dispatcherLogStr: null,
+            isLoading: true,
+            logDrawerHeight: window.innerHeight - 48
         };
     }
 
-    getNNImanagerLogmessage = () => {
-        if (this._isLogDrawer === true) {
-            this.setState({ isLoading: true }, () => {
-                axios(`${DOWNLOAD_IP}/nnimanager.log`, {
-                    method: 'GET'
-                })
-                    .then(res => {
-                        if (res.status === 200) {
-                            setTimeout(() => { this.setNNImanager(res.data); }, 300);
-                        }
-                    });
-            });
-        }
-    }
-
-    setDispatcher = (value: string) => {
-        if (this._isLogDrawer === true) {
-            this.setState({ isLoadispatcher: false, dispatcherLogStr: value });
-        }
-    }
-
-    setNNImanager = (val: string) => {
-        if (this._isLogDrawer === true) {
-            this.setState({ isLoading: false, nniManagerLogStr: val });
-        }
-    }
-
-    getdispatcherLogmessage = () => {
-        if (this._isLogDrawer === true) {
-            this.setState({ isLoadispatcher: true }, () => {
-                axios(`${DOWNLOAD_IP}/dispatcher.log`, {
-                    method: 'GET'
-                })
-                    .then(res => {
-                        if (res.status === 200) {
-                            setTimeout(() => { this.setDispatcher(res.data); }, 300);
-                        }
-                    });
-            });
-        }
-    }
-
     downloadNNImanager = () => {
-        const { nniManagerLogStr } = this.state;
-        downFile(nniManagerLogStr, 'nnimanager.log');
+        if (this.state.nniManagerLogStr !== null) {
+            downFile(this.state.nniManagerLogStr, 'nnimanager.log');
+        }
     }
 
     downloadDispatcher = () => {
-        const { dispatcherLogStr } = this.state;
-        downFile(dispatcherLogStr, 'dispatcher.log');
+        if (this.state.dispatcherLogStr !== null) {
+            downFile(this.state.dispatcherLogStr, 'dispatcher.log');
+        }
     }
 
     dispatcherHTML = () => {
         return (
             <div>
                 <span>Dispatcher Log</span>
-                <span className="refresh" onClick={this.getdispatcherLogmessage}>
+                <span className="refresh" onClick={this.manualRefresh}>
                     <Icon type="sync" />
                 </span>
             </div>
@@ -101,37 +60,28 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
         return (
             <div>
                 <span>NNImanager Log</span>
-                <span className="refresh" onClick={this.getNNImanagerLogmessage}><Icon type="sync" /></span>
+                <span className="refresh" onClick={this.manualRefresh}><Icon type="sync" /></span>
             </div>
         );
     }
 
-    componentDidMount() {
-        this._isLogDrawer = true;
-        this.getNNImanagerLogmessage();
-        this.getdispatcherLogmessage();
+    setLogDrawerHeight = () => {
+        this.setState(() => ({ logDrawerHeight: window.innerHeight - 48 }));
     }
 
-    componentWillReceiveProps(nextProps: LogDrawerProps) {
-        const { isVisble, activeTab } = nextProps;
-        if (isVisble === true) {
-            if (activeTab === 'nnimanager') {
-                this.getNNImanagerLogmessage();
-            }
-            if (activeTab === 'dispatcher') {
-                this.getdispatcherLogmessage();
-            }
-        }
+    async componentDidMount() {
+        this.refresh();
+        window.addEventListener('resize', this.setLogDrawerHeight);
     }
 
     componentWillUnmount() {
-        this._isLogDrawer = false;
+        window.clearTimeout(this.timerId);
+        window.removeEventListener('resize', this.setLogDrawerHeight);
     }
 
     render() {
-        const { isVisble, closeDrawer, activeTab } = this.props;
-        const { nniManagerLogStr, dispatcherLogStr, isLoadispatcher, isLoading } = this.state;
-        const heights: number = window.innerHeight - 48; // padding top and bottom
+        const { closeDrawer, activeTab } = this.props;
+        const { nniManagerLogStr, dispatcherLogStr, isLoading, logDrawerHeight } = this.state;
         return (
             <Row>
                 <Drawer
@@ -139,18 +89,26 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
                     closable={false}
                     destroyOnClose={true}
                     onClose={closeDrawer}
-                    visible={isVisble}
+                    visible={true}
                     width="76%"
-                    height={heights}
+                    height={logDrawerHeight}
                 // className="logDrawer"
                 >
-                    <div className="card-container log-tab-body" style={{ height: heights }}>
-                        <Tabs type="card" defaultActiveKey={activeTab} style={{ height: heights + 19 }}>
+                    <div className="card-container log-tab-body">
+                        <Tabs
+                            type="card"
+                            defaultActiveKey={activeTab}
+                            style={{ height: logDrawerHeight, minHeight: 190 }}
+                        >
                             {/* <Tabs type="card" onTabClick={this.selectwhichLog} defaultActiveKey={activeTab}> */}
                             {/* <TabPane tab="Dispatcher Log" key="dispatcher"> */}
                             <TabPane tab={this.dispatcherHTML()} key="dispatcher">
                                 <div>
-                                    <MonacoHTML content={dispatcherLogStr} loading={isLoadispatcher} />
+                                    <MonacoHTML
+                                        content={dispatcherLogStr || 'Loading...'}
+                                        loading={isLoading}
+                                        height={logDrawerHeight - 104}
+                                    />
                                 </div>
                                 <Row className="buttons">
                                     <Col span={12} className="download">
@@ -174,7 +132,11 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
                             <TabPane tab={this.nnimanagerHTML()} key="nnimanager">
                                 {/* <TabPane tab="NNImanager Log" key="nnimanager"> */}
                                 <div>
-                                    <MonacoHTML content={nniManagerLogStr} loading={isLoading} />
+                                    <MonacoHTML
+                                        content={nniManagerLogStr || 'Loading...'}
+                                        loading={isLoading}
+                                        height={logDrawerHeight - 104}
+                                    />
                                 </div>
                                 <Row className="buttons">
                                     <Col span={12} className="download">
@@ -201,6 +163,31 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
             </Row>
         );
     }
+
+    private refresh = () => {
+        window.clearTimeout(this.timerId);
+        const dispatcherPromise = axios.get(`${DOWNLOAD_IP}/dispatcher.log`);
+        const nniManagerPromise = axios.get(`${DOWNLOAD_IP}/nnimanager.log`);
+        dispatcherPromise.then(res => {
+            if (res.status === 200) {
+                this.setState({ dispatcherLogStr: res.data });
+            }
+        });
+        nniManagerPromise.then(res => {
+            if (res.status === 200) {
+                this.setState({ nniManagerLogStr: res.data });
+            }
+        });
+        Promise.all([dispatcherPromise, nniManagerPromise]).then(() => {
+            this.setState({ isLoading: false });
+            this.timerId = window.setTimeout(this.refresh, 300);
+        });
+    }
+
+    private manualRefresh = () => {
+        this.setState({ isLoading: true });
+        this.refresh();
+    }
 }
 
 export default LogDrawer;

src/webui/src/components/SlideBar.tsx

@@ -214,7 +214,12 @@ class SlideBar extends React.Component<SliderProps, SliderState> {
                             type="ghost"
                         >
                             <a target="_blank" href="https://nni.readthedocs.io/en/latest/Tutorial/WebUI.html">
-                                <Icon type="question" /><span>Help</span>
+                                <img
+                                    src={require('../static/img/icon/ques.png')}
+                                    alt="question"
+                                    className="question"
+                                />
+                                <span>Help</span>
                             </a>
                         </Button>
                     </span>
@@ -329,8 +334,8 @@ class SlideBar extends React.Component<SliderProps, SliderState> {
 
     render() {
         const mobile = (<MediaQuery maxWidth={884}>{this.mobileHTML()}</MediaQuery>);
-        const tablet = (<MediaQuery minWidth={885} maxWidth={1241}>{this.tabeltHTML()}</MediaQuery>);
-        const desktop = (<MediaQuery minWidth={1242}>{this.desktopHTML()}</MediaQuery>);
+        const tablet = (<MediaQuery minWidth={885} maxWidth={1281}>{this.tabeltHTML()}</MediaQuery>);
+        const desktop = (<MediaQuery minWidth={1282}>{this.desktopHTML()}</MediaQuery>);
         const { isvisibleLogDrawer, activeKey, isvisibleExperimentDrawer } = this.state;
         return (
             <div>
@@ -338,11 +343,12 @@ class SlideBar extends React.Component<SliderProps, SliderState> {
                 {tablet}
                 {desktop}
                 {/* the drawer for dispatcher & nnimanager log message */}
+                {isvisibleLogDrawer ? (
                     <LogDrawer
-                    isVisble={isvisibleLogDrawer}
                         closeDrawer={this.closeLogDrawer}
                         activeTab={activeKey}
                     />
+                ) : null}
                 <ExperimentDrawer
                     isVisble={isvisibleExperimentDrawer}
                     closeExpDrawer={this.closeExpDrawer}

src/webui/src/components/TrialsDetail.tsx

@@ -100,10 +100,6 @@ class TrialsDetail extends React.Component<TrialsDetailProps, TrialDetailState>
         this.setState({ whichGraph: activeKey });
     }
 
-    test = () => {
-        alert('TableList component was not properly initialized.');
-    }
-
     updateSearchFilterType = (value: string) => {
         // clear input value and re-render table
         if (this.searchInput !== null) {
@@ -167,14 +163,14 @@ class TrialsDetail extends React.Component<TrialsDetailProps, TrialDetailState>
                     <Col span={14} className="right">
                         <Button
                             className="common"
-                            onClick={this.tableList ? this.tableList.addColumn : this.test}
+                            onClick={() => { if (this.tableList) { this.tableList.addColumn(); }}}
                         >
                             Add column
                         </Button>
                         <Button
                             className="mediateBtn common"
                             // use child-component tableList's function, the function is in child-component.
-                            onClick={this.tableList ? this.tableList.compareBtn : this.test}
+                            onClick={() => { if (this.tableList) { this.tableList.compareBtn(); }}}
                         >
                             Compare
                         </Button>

src/webui/src/components/overview/Progress.tsx

@@ -184,11 +184,12 @@ class Progressed extends React.Component<ProgressProps, ProgressState> {
                     </Col>
                 </Row>
                 {/* learn about click -> default active key is dispatcher. */}
+                {isShowLogDrawer ? (
                     <LogDrawer
-                    isVisble={isShowLogDrawer}
                         closeDrawer={this.closeDrawer}
                         activeTab="dispatcher"
                     />
+                ) : null}
             </Row>
         );
     }

src/webui/src/components/public-child/MonacoEditor.tsx

@@ -6,6 +6,7 @@ import MonacoEditor from 'react-monaco-editor';
 interface MonacoEditorProps {
     content: string;
     loading: boolean;
+    height: number;
 }
 
 class MonacoHTML extends React.Component<MonacoEditorProps, {}> {
@@ -25,18 +26,17 @@ class MonacoHTML extends React.Component<MonacoEditorProps, {}> {
     }
 
     render() {
-        const { content, loading } = this.props;
-        const heights: number = window.innerHeight - 48;
+        const { content, loading, height } = this.props;
         return (
             <div className="just-for-log">
                 <Spin
                     // tip="Loading..."
-                    style={{ width: '100%', height: heights * 0.9 }}
+                    style={{ width: '100%', height: height }}
                     spinning={loading}
                 >
                     <MonacoEditor
                         width="100%"
-                        height={heights * 0.9}
+                        height={height}
                         language="json"
                         value={content}
                         options={DRAWEROPTION}

src/webui/src/components/trial-detail/DefaultMetricPoint.tsx

@@ -135,17 +135,17 @@ function generateScatterSeries(trials: Trial[]) {
 
 function generateBestCurveSeries(trials: Trial[]) {
     let best = trials[0];
-    const data = [[ best.sequenceId, best.accuracy, best.info.hyperParameters ]];
+    const data = [[ best.sequenceId, best.accuracy, best.description.parameters ]];
 
     for (let i = 1; i < trials.length; i++) {
         const trial = trials[i];
         const delta = trial.accuracy! - best.accuracy!;
         const better = (EXPERIMENT.optimizeMode === 'minimize') ? (delta < 0) : (delta > 0);
         if (better) {
-            data.push([ trial.sequenceId, trial.accuracy, trial.info.hyperParameters ]);
+            data.push([ trial.sequenceId, trial.accuracy, trial.description.parameters ]);
             best = trial;
         } else {
-            data.push([ trial.sequenceId, best.accuracy, trial.info.hyperParameters ]);
+            data.push([ trial.sequenceId, best.accuracy, trial.description.parameters ]);
         }
     }