Merge pull request #218 from microsoft/master

merge master

src/sdk/pynni/tests/test_compressor.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from unittest import TestCase, main
 import numpy as np
 import tensorflow as tf
 import torch
 import torch.nn.functional as F
 import nni.compression.torch as torch_compressor
+import math
 
 if tf.__version__ >= '2.0':
     import nni.compression.tensorflow as tf_compressor
 
+
 def get_tf_model():
     model = tf.keras.models.Sequential([
         tf.keras.layers.Conv2D(filters=5, kernel_size=7, input_shape=[28, 28, 1], activation='relu', padding="SAME"),
@@ -20,43 +25,70 @@ def get_tf_model():
         tf.keras.layers.Dense(units=10, activation='softmax'),
     ])
     model.compile(loss="sparse_categorical_crossentropy",
-        optimizer=tf.keras.optimizers.SGD(lr=1e-3),
-        metrics=["accuracy"])
+                  optimizer=tf.keras.optimizers.SGD(lr=1e-3),
+                  metrics=["accuracy"])
     return model
 
+
 class TorchModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
         self.conv1 = torch.nn.Conv2d(1, 5, 5, 1)
+        self.bn1 = torch.nn.BatchNorm2d(5)
         self.conv2 = torch.nn.Conv2d(5, 10, 5, 1)
+        self.bn2 = torch.nn.BatchNorm2d(10)
         self.fc1 = torch.nn.Linear(4 * 4 * 10, 100)
         self.fc2 = torch.nn.Linear(100, 10)
 
     def forward(self, x):
-        x = F.relu(self.conv1(x))
+        x = F.relu(self.bn1(self.conv1(x)))
         x = F.max_pool2d(x, 2, 2)
-        x = F.relu(self.conv2(x))
+        x = F.relu(self.bn2(self.conv2(x)))
         x = F.max_pool2d(x, 2, 2)
         x = x.view(-1, 4 * 4 * 10)
         x = F.relu(self.fc1(x))
         x = self.fc2(x)
         return F.log_softmax(x, dim=1)
 
+
 def tf2(func):
     def test_tf2_func(*args):
         if tf.__version__ >= '2.0':
             func(*args)
+
     return test_tf2_func
 
-k1 = [[1]*3]*3
-k2 = [[2]*3]*3
-k3 = [[3]*3]*3
-k4 = [[4]*3]*3
-k5 = [[5]*3]*3
+# for fpgm filter pruner test
+w = np.array([[[[i+1]*3]*3]*5 for i in range(10)])
 
-w = [[k1, k2, k3, k4, k5]] * 10
 
 class CompressorTestCase(TestCase):
+    def test_torch_quantizer_modules_detection(self):
+        # test if modules can be detected
+        model = TorchModel()
+        config_list = [{
+            'quant_types': ['weight'],
+            'quant_bits': 8,
+            'op_types':['Conv2d', 'Linear']
+        }, {
+            'quant_types': ['output'],
+            'quant_bits': 8,
+            'quant_start_step': 0,
+            'op_types':['ReLU']
+        }]
+
+        model.relu = torch.nn.ReLU()
+        quantizer = torch_compressor.QAT_Quantizer(model, config_list)
+        quantizer.compress()
+        modules_to_compress = quantizer.get_modules_to_compress()
+        modules_to_compress_name = [ t[0].name for t in modules_to_compress]
+        assert "conv1" in modules_to_compress_name
+        assert "conv2" in modules_to_compress_name
+        assert "fc1" in modules_to_compress_name
+        assert "fc2" in modules_to_compress_name
+        assert "relu" in modules_to_compress_name
+        assert len(modules_to_compress_name) == 5
+
     def test_torch_level_pruner(self):
         model = TorchModel()
         configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
@@ -74,7 +106,7 @@ class CompressorTestCase(TestCase):
             'quant_bits': {
                 'weight': 8,
             },
-            'op_types':['Conv2d', 'Linear']
+            'op_types': ['Conv2d', 'Linear']
         }]
         torch_compressor.NaiveQuantizer(model, configure_list).compress()
 
@@ -84,16 +116,16 @@ class CompressorTestCase(TestCase):
 
     def test_torch_fpgm_pruner(self):
         """
-        With filters(kernels) defined as above (k1 - k5), it is obvious that k3 is the Geometric Median
+        With filters(kernels) weights defined as above (w), it is obvious that w[4] and w[5] is the Geometric Median
         which minimize the total geometric distance by defination of Geometric Median in this paper:
         Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration,
         https://arxiv.org/pdf/1811.00250.pdf
 
-        So if sparsity is 0.2, the expected masks should mask out all k3, this can be verified through:
-        `all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 90., 0., 90., 90.]))`
+        So if sparsity is 0.2, the expected masks should mask out w[4] and w[5], this can be verified through:
+        `all(torch.sum(masks, (1, 2, 3)).numpy() == np.array([45., 45., 45., 45., 0., 0., 45., 45., 45., 45.]))`
 
-        If sparsity is 0.6, the expected masks should mask out all k2, k3, k4, this can be verified through:
-        `all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 0., 0., 0., 90.]))`
+        If sparsity is 0.6, the expected masks should mask out w[2] - w[7], this can be verified through:
+        `all(torch.sum(masks, (1, 2, 3)).numpy() == np.array([45., 45., 0., 0., 0., 0., 0., 0., 45., 45.]))`
         """
 
         model = TorchModel()
@@ -103,12 +135,12 @@ class CompressorTestCase(TestCase):
         model.conv2.weight.data = torch.tensor(w).float()
         layer = torch_compressor.compressor.LayerInfo('conv2', model.conv2)
         masks = pruner.calc_mask(layer, config_list[0])
-        assert all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 90., 0., 90., 90.]))
+        assert all(torch.sum(masks, (1, 2, 3)).numpy() == np.array([45., 45., 45., 45., 0., 0., 45., 45., 45., 45.]))
 
         pruner.update_epoch(1)
         model.conv2.weight.data = torch.tensor(w).float()
         masks = pruner.calc_mask(layer, config_list[1])
-        assert all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 0., 0., 0., 90.]))
+        assert all(torch.sum(masks, (1, 2, 3)).numpy() == np.array([45., 45., 0., 0., 0., 0., 0., 0., 45., 45.]))
 
     @tf2
     def test_tf_fpgm_pruner(self):
@@ -122,17 +154,122 @@ class CompressorTestCase(TestCase):
 
         layer = tf_compressor.compressor.LayerInfo(model.layers[2])
         masks = pruner.calc_mask(layer, config_list[0]).numpy()
-        masks = masks.transpose([2, 3, 0, 1]).transpose([1, 0, 2, 3])
+        masks = masks.reshape((-1, masks.shape[-1])).transpose([1, 0])
 
-        assert all(masks.sum((0, 2, 3)) == np.array([90., 90., 0., 90., 90.]))
+        assert all(masks.sum((1)) == np.array([45., 45., 45., 45., 0., 0., 45., 45., 45., 45.]))
 
         pruner.update_epoch(1)
         model.layers[2].set_weights([weights[0], weights[1].numpy()])
         masks = pruner.calc_mask(layer, config_list[1]).numpy()
-        masks = masks.transpose([2, 3, 0, 1]).transpose([1, 0, 2, 3])
+        masks = masks.reshape((-1, masks.shape[-1])).transpose([1, 0])
+        assert all(masks.sum((1)) == np.array([45., 45., 0., 0., 0., 0., 0., 0., 45., 45.]))
+
+    def test_torch_l1filter_pruner(self):
+        """
+        Filters with the minimum sum of the weights' L1 norm are pruned in this paper:
+        PRUNING FILTERS FOR EFFICIENT CONVNETS,
+        https://arxiv.org/abs/1608.08710
+
+        So if sparsity is 0.2, the expected masks should mask out filter 0, this can be verified through:
+        `all(torch.sum(mask1, (1, 2, 3)).numpy() == np.array([0., 27., 27., 27., 27.]))`
+
+        If sparsity is 0.6, the expected masks should mask out filter 0,1,2, this can be verified through:
+        `all(torch.sum(mask2, (1, 2, 3)).numpy() == np.array([0., 0., 0., 27., 27.]))`
+        """
+        w = np.array([np.zeros((3, 3, 3)), np.ones((3, 3, 3)), np.ones((3, 3, 3)) * 2,
+                      np.ones((3, 3, 3)) * 3, np.ones((3, 3, 3)) * 4])
+        model = TorchModel()
+        config_list = [{'sparsity': 0.2, 'op_names': ['conv1']}, {'sparsity': 0.6, 'op_names': ['conv2']}]
+        pruner = torch_compressor.L1FilterPruner(model, config_list)
+
+        model.conv1.weight.data = torch.tensor(w).float()
+        model.conv2.weight.data = torch.tensor(w).float()
+        layer1 = torch_compressor.compressor.LayerInfo('conv1', model.conv1)
+        mask1 = pruner.calc_mask(layer1, config_list[0])
+        layer2 = torch_compressor.compressor.LayerInfo('conv2', model.conv2)
+        mask2 = pruner.calc_mask(layer2, config_list[1])
+        assert all(torch.sum(mask1, (1, 2, 3)).numpy() == np.array([0., 27., 27., 27., 27.]))
+        assert all(torch.sum(mask2, (1, 2, 3)).numpy() == np.array([0., 0., 0., 27., 27.]))
+
+    def test_torch_slim_pruner(self):
+        """
+        Scale factors with minimum l1 norm in the BN layers are pruned in this paper:
+        Learning Efficient Convolutional Networks through Network Slimming,
+        https://arxiv.org/pdf/1708.06519.pdf
+
+        So if sparsity is 0.2, the expected masks should mask out channel 0, this can be verified through:
+        `all(mask1.numpy() == np.array([0., 1., 1., 1., 1.]))`
+        `all(mask2.numpy() == np.array([0., 1., 1., 1., 1.]))`
+
+        If sparsity is 0.6, the expected masks should mask out channel 0,1,2, this can be verified through:
+        `all(mask1.numpy() == np.array([0., 0., 0., 1., 1.]))`
+        `all(mask2.numpy() == np.array([0., 0., 0., 1., 1.]))`
+        """
+        w = np.array([0, 1, 2, 3, 4])
+        model = TorchModel()
+        config_list = [{'sparsity': 0.2, 'op_types': ['BatchNorm2d']}]
+        model.bn1.weight.data = torch.tensor(w).float()
+        model.bn2.weight.data = torch.tensor(-w).float()
+        pruner = torch_compressor.SlimPruner(model, config_list)
+
+        layer1 = torch_compressor.compressor.LayerInfo('bn1', model.bn1)
+        mask1 = pruner.calc_mask(layer1, config_list[0])
+        layer2 = torch_compressor.compressor.LayerInfo('bn2', model.bn2)
+        mask2 = pruner.calc_mask(layer2, config_list[0])
+        assert all(mask1.numpy() == np.array([0., 1., 1., 1., 1.]))
+        assert all(mask2.numpy() == np.array([0., 1., 1., 1., 1.]))
+
+        config_list = [{'sparsity': 0.6, 'op_types': ['BatchNorm2d']}]
+        model.bn1.weight.data = torch.tensor(w).float()
+        model.bn2.weight.data = torch.tensor(w).float()
+        pruner = torch_compressor.SlimPruner(model, config_list)
+
+        layer1 = torch_compressor.compressor.LayerInfo('bn1', model.bn1)
+        mask1 = pruner.calc_mask(layer1, config_list[0])
+        layer2 = torch_compressor.compressor.LayerInfo('bn2', model.bn2)
+        mask2 = pruner.calc_mask(layer2, config_list[0])
+        assert all(mask1.numpy() == np.array([0., 0., 0., 1., 1.]))
+        assert all(mask2.numpy() == np.array([0., 0., 0., 1., 1.]))
+
+    def test_torch_QAT_quantizer(self):
+        model = TorchModel()
+        config_list = [{
+            'quant_types': ['weight'],
+            'quant_bits': 8,
+            'op_types':['Conv2d', 'Linear']
+        }, {
+            'quant_types': ['output'],
+            'quant_bits': 8,
+            'quant_start_step': 0,
+            'op_types':['ReLU']
+        }]
+        model.relu = torch.nn.ReLU()
+        quantizer = torch_compressor.QAT_Quantizer(model, config_list)
+        quantizer.compress()
+        # test quantize
+        # range not including 0
+        eps = 1e-7
+        weight = torch.tensor([[1, 2], [3, 5]]).float()
+        quantize_weight = quantizer.quantize_weight(weight, config_list[0], model.conv2)
+        assert math.isclose(model.conv2.scale, 5 / 255, abs_tol=eps)
+        assert model.conv2.zero_point == 0
+         # range including 0
+        weight = torch.tensor([[-1, 2], [3, 5]]).float()
+        quantize_weight = quantizer.quantize_weight(weight, config_list[0], model.conv2)
+        assert math.isclose(model.conv2.scale, 6 / 255, abs_tol=eps)
+        assert model.conv2.zero_point in (42, 43)
 
-        assert all(masks.sum((0, 2, 3)) == np.array([90., 0., 0., 0., 90.]))
+        # test ema
+        x = torch.tensor([[-0.2, 0], [0.1, 0.2]])
+        out = model.relu(x)
+        assert math.isclose(model.relu.tracked_min_biased, 0, abs_tol=eps)
+        assert math.isclose(model.relu.tracked_max_biased, 0.002, abs_tol=eps)
 
+        quantizer.step()
+        x = torch.tensor([[0.2, 0.4], [0.6, 0.8]])
+        out = model.relu(x)
+        assert math.isclose(model.relu.tracked_min_biased, 0.002, abs_tol=eps)
+        assert math.isclose(model.relu.tracked_max_biased, 0.00998, abs_tol=eps)
 
 if __name__ == '__main__':
     main()

src/sdk/pynni/tests/test_msg_dispatcher.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.
-# ==================================================================================================
-
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import json
 from io import BytesIO

src/sdk/pynni/tests/test_protocol.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.
-# ==================================================================================================
-
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import nni.protocol
 from nni.protocol import CommandType, send, receive

src/sdk/pynni/tests/test_smartparam.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.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import os
 

src/sdk/pynni/tests/test_trial.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.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import nni
 import nni.platform.test as test_platform
@@ -93,4 +76,4 @@ class TrialTestCase(TestCase):
 
 
 if __name__ == '__main__':
-    main()
+    main()
\ No newline at end of file

src/sdk/pynni/tests/test_utils.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.
-# ==================================================================================================
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 from unittest import TestCase, main
 
@@ -102,4 +85,4 @@ class UtilsTestCase(TestCase):
 
 
 if __name__ == '__main__':
-    main()
+    main()
\ No newline at end of file

src/webui/package.json

@@ -12,7 +12,7 @@
     "copy-to-clipboard": "^3.0.8",
     "css-loader": "0.28.7",
     "dotenv": "^8.0.0",
-    "echarts": "^4.1.0",
+    "echarts": "^4.5.0",
     "echarts-for-react": "^2.0.14",
     "file-loader": "^4.1.0",
     "fork-ts-checker-webpack-plugin": "^1.5.0",

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

@@ -3,7 +3,7 @@ import { Switch } from 'antd';
 import ReactEcharts from 'echarts-for-react';
 import { EXPERIMENT, TRIALS } from '../../static/datamodel';
 import { Trial } from '../../static/model/trial';
-import { TooltipForAccuracy } from '../../static/interface';
+import { TooltipForAccuracy, EventMap } from '../../static/interface';
 require('echarts/lib/chart/scatter');
 require('echarts/lib/component/tooltip');
 require('echarts/lib/component/title');
@@ -16,12 +16,18 @@ interface DefaultPointProps {
 
 interface DefaultPointState {
     bestCurveEnabled: boolean;
+    startY: number;  // dataZoomY
+    endY: number;
 }
 
 class DefaultPoint extends React.Component<DefaultPointProps, DefaultPointState> {
     constructor(props: DefaultPointProps) {
         super(props);
-        this.state = { bestCurveEnabled: false };
+        this.state = {
+            bestCurveEnabled: false,
+            startY: 0, // dataZoomY
+            endY: 100,
+        };
     }
 
     loadDefault = (checked: boolean) => {
@@ -35,6 +41,7 @@ class DefaultPoint extends React.Component<DefaultPointProps, DefaultPointState>
     render() {
         const graph = this.generateGraph();
         const accNodata = (graph === EmptyGraph ? 'No data' : '');
+        const onEvents = { 'dataZoom': this.metricDataZoom };
 
         return (
             <div>
@@ -53,6 +60,7 @@ class DefaultPoint extends React.Component<DefaultPointProps, DefaultPointState>
                     }}
                     theme="my_theme"
                     notMerge={true} // update now
+                    onEvents={onEvents}
                 />
                 <div className="showMess">{accNodata}</div>
             </div>
@@ -64,14 +72,66 @@ class DefaultPoint extends React.Component<DefaultPointProps, DefaultPointState>
         if (trials.length === 0) {
             return EmptyGraph;
         }
-        const graph = generateGraphConfig(trials[trials.length - 1].sequenceId);
+        const graph = this.generateGraphConfig(trials[trials.length - 1].sequenceId);
         if (this.state.bestCurveEnabled) {
-            (graph as any).series = [ generateBestCurveSeries(trials), generateScatterSeries(trials) ];
+            (graph as any).series = [generateBestCurveSeries(trials), generateScatterSeries(trials)];
         } else {
-            (graph as any).series = [ generateScatterSeries(trials) ];
+            (graph as any).series = [generateScatterSeries(trials)];
         }
         return graph;
     }
+
+    private generateGraphConfig(maxSequenceId: number) {
+        const { startY, endY } = this.state;
+        return {
+            grid: {
+                left: '8%',
+            },
+            tooltip: {
+                trigger: 'item',
+                enterable: true,
+                position: (point: Array<number>, data: TooltipForAccuracy) => (
+                    [(data.data[0] < maxSequenceId ? point[0] : (point[0] - 300)), 80]
+                ),
+                formatter: (data: TooltipForAccuracy) => (
+                    '<div class="tooldetailAccuracy">' +
+                    '<div>Trial No.: ' + data.data[0] + '</div>' +
+                    '<div>Default metric: ' + data.data[1] + '</div>' +
+                    '<div>Parameters: <pre>' + JSON.stringify(data.data[2], null, 4) + '</pre></div>' +
+                    '</div>'
+                ),
+            },
+            dataZoom: [
+                {
+                    id: 'dataZoomY',
+                    type: 'inside',
+                    yAxisIndex: [0],
+                    filterMode: 'empty',
+                    start: startY,
+                    end: endY
+                }
+            ],
+            xAxis: {
+                name: 'Trial',
+                type: 'category',
+            },
+            yAxis: {
+                name: 'Default metric',
+                type: 'value',
+                scale: true,
+            },
+            series: undefined,
+        };
+    }
+
+    private metricDataZoom = (e: EventMap) => {
+        if (e.batch !== undefined) {
+            this.setState(() => ({
+                startY: (e.batch[0].start !== null ? e.batch[0].start : 0),
+                endY: (e.batch[0].end !== null ? e.batch[0].end : 100)
+            }));
+        }
+    }
 }
 
 const EmptyGraph = {
@@ -85,41 +145,10 @@ const EmptyGraph = {
     yAxis: {
         name: 'Default metric',
         type: 'value',
+        scale: true,
     }
 };
 
-function generateGraphConfig(maxSequenceId: number) {
-    return {
-        grid: {
-            left: '8%',
-        },
-        tooltip: {
-            trigger: 'item',
-            enterable: true,
-            position: (point: Array<number>, data: TooltipForAccuracy) => (
-                [ (data.data[0] < maxSequenceId ? point[0] : (point[0] - 300)), 80 ]
-            ),
-            formatter: (data: TooltipForAccuracy) => (
-                '<div class="tooldetailAccuracy">' +
-                '<div>Trial No.: ' + data.data[0] + '</div>' +
-                '<div>Default metric: ' + data.data[1] + '</div>' +
-                '<div>Parameters: <pre>' + JSON.stringify(data.data[2], null, 4) + '</pre></div>' +
-                '</div>'
-            ),
-        },
-        xAxis: {
-            name: 'Trial',
-            type: 'category',
-        },
-        yAxis: {
-            name: 'Default metric',
-            type: 'value',
-            scale: true,
-        },
-        series: undefined,
-    };
-}
-
 function generateScatterSeries(trials: Trial[]) {
     const data = trials.map(trial => [
         trial.sequenceId,
@@ -135,17 +164,17 @@ function generateScatterSeries(trials: Trial[]) {
 
 function generateBestCurveSeries(trials: Trial[]) {
     let best = trials[0];
-    const data = [[ best.sequenceId, best.accuracy, best.description.parameters ]];
+    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.description.parameters ]);
+            data.push([trial.sequenceId, trial.accuracy, trial.description.parameters]);
             best = trial;
         } else {
-            data.push([ trial.sequenceId, best.accuracy, trial.description.parameters ]);
+            data.push([trial.sequenceId, best.accuracy, trial.description.parameters]);
         }
     }
 

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

 import * as React from 'react';
 import ReactEcharts from 'echarts-for-react';
-import { TableObj } from 'src/static/interface';
+import { TableObj, EventMap } from 'src/static/interface';
 import { filterDuration } from 'src/static/function';
 require('echarts/lib/chart/bar');
 require('echarts/lib/component/tooltip');
@@ -17,7 +17,8 @@ interface DurationProps {
 }
 
 interface DurationState {
-    durationSource: {};
+    startDuration: number; // for record data zoom
+    endDuration: number;
 }
 
 class Duration extends React.Component<DurationProps, DurationState> {
@@ -26,63 +27,13 @@ class Duration extends React.Component<DurationProps, DurationState> {
 
         super(props);
         this.state = {
-            durationSource: this.initDuration(this.props.source),
-        };
-
-    }
-
-    initDuration = (source: Array<TableObj>) => {
-        const trialId: Array<string> = [];
-        const trialTime: Array<number> = [];
-        const trialJobs = source.filter(filterDuration);
-        Object.keys(trialJobs).map(item => {
-            const temp = trialJobs[item];
-            trialId.push(temp.sequenceId);
-            trialTime.push(temp.duration);
-        });
-        return {
-            tooltip: {
-                trigger: 'axis',
-                axisPointer: {
-                    type: 'shadow'
-                }
-            },
-            grid: {
-                bottom: '3%',
-                containLabel: true,
-                left: '1%',
-                right: '4%'
-            },
-
-            dataZoom: [{
-                type: 'slider',
-                name: 'trial',
-                filterMode: 'filter',
-                yAxisIndex: 0,
-                orient: 'vertical'
-            }, {
-                type: 'slider',
-                name: 'trial',
-                filterMode: 'filter',
-                xAxisIndex: 0
-            }],
-            xAxis: {
-                name: 'Time',
-                type: 'value',
-            },
-            yAxis: {
-                name: 'Trial',
-                type: 'category',
-                data: trialId
-            },
-            series: [{
-                type: 'bar',
-                data: trialTime
-            }]
+            startDuration: 0, // for record data zoom
+            endDuration: 100,
         };
     }
 
     getOption = (dataObj: Runtrial) => {
+        const { startDuration, endDuration } = this.state;
         return {
             tooltip: {
                 trigger: 'axis',
@@ -96,19 +47,16 @@ class Duration extends React.Component<DurationProps, DurationState> {
                 left: '1%',
                 right: '4%'
             },
-
-            dataZoom: [{
-                type: 'slider',
-                name: 'trial',
-                filterMode: 'filter',
-                yAxisIndex: 0,
-                orient: 'vertical'
-            }, {
-                type: 'slider',
-                name: 'trial',
-                filterMode: 'filter',
-                xAxisIndex: 0
-            }],
+            dataZoom: [
+                {
+                    id: 'dataZoomY',
+                    type: 'inside',
+                    yAxisIndex: [0],
+                    filterMode: 'empty',
+                    start: startDuration,
+                    end: endDuration
+                },
+            ],
             xAxis: {
                 name: 'Time',
                 type: 'value',
@@ -140,21 +88,7 @@ class Duration extends React.Component<DurationProps, DurationState> {
             trialId: trialId,
             trialTime: trialTime
         });
-        this.setState({
-            durationSource: this.getOption(trialRun[0])
-        });
-    }
-
-    componentDidMount() {
-        const { source } = this.props;
-        this.drawDurationGraph(source);
-    }
-
-    componentWillReceiveProps(nextProps: DurationProps) {
-        const { whichGraph, source } = nextProps;
-        if (whichGraph === '3') {
-            this.drawDurationGraph(source);
-        }
+        return this.getOption(trialRun[0]);
     }
 
     shouldComponentUpdate(nextProps: DurationProps, nextState: DurationState) {
@@ -183,18 +117,31 @@ class Duration extends React.Component<DurationProps, DurationState> {
     }
 
     render() {
-        const { durationSource } = this.state;
+
+        const { source } = this.props;
+        const graph = this.drawDurationGraph(source);
+        const onEvents = { 'dataZoom': this.durationDataZoom };
         return (
             <div>
                 <ReactEcharts
-                    option={durationSource}
+                    option={graph}
                     style={{ width: '95%', height: 412, margin: '0 auto' }}
                     theme="my_theme"
                     notMerge={true} // update now
+                    onEvents={onEvents}
                 />
             </div>
         );
     }
+
+    private durationDataZoom = (e: EventMap) => {
+        if (e.batch !== undefined) {
+            this.setState(() => ({
+                startDuration: (e.batch[0].start !== null ? e.batch[0].start : 0),
+                endDuration: (e.batch[0].end !== null ? e.batch[0].end : 100)
+            }));
+        }
+    }
 }
 
 export default Duration;

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

 import * as React from 'react';
 import { Row, Button, Switch } from 'antd';
-import { TooltipForIntermediate, TableObj, Intermedia } from '../../static/interface';
+import { TooltipForIntermediate, TableObj, Intermedia, EventMap } from '../../static/interface';
 import ReactEcharts from 'echarts-for-react';
 require('echarts/lib/component/tooltip');
 require('echarts/lib/component/title');
@@ -14,6 +14,8 @@ interface IntermediateState {
     isFilter: boolean;
     length: number;
     clickCounts: number; // user filter intermediate click confirm btn's counts
+    startMediaY: number;
+    endMediaY: number;
 }
 
 interface IntermediateProps {
@@ -38,7 +40,9 @@ class Intermediate extends React.Component<IntermediateProps, IntermediateState>
             isLoadconfirmBtn: false,
             isFilter: false,
             length: 100000,
-            clickCounts: 0
+            clickCounts: 0,
+            startMediaY: 0,
+            endMediaY: 100
         };
     }
 
@@ -48,6 +52,7 @@ class Intermediate extends React.Component<IntermediateProps, IntermediateState>
                 length: source.length,
                 detailSource: source
             });
+            const { startMediaY, endMediaY } = this.state;
             const trialIntermediate: Array<Intermedia> = [];
             Object.keys(source).map(item => {
                 const temp = source[item];
@@ -113,6 +118,16 @@ class Intermediate extends React.Component<IntermediateProps, IntermediateState>
                     type: 'value',
                     name: 'Metric'
                 },
+                dataZoom: [
+                    {
+                        id: 'dataZoomY',
+                        type: 'inside',
+                        yAxisIndex: [0],
+                        filterMode: 'empty',
+                        start: startMediaY,
+                        end: endMediaY
+                    }
+                ],
                 series: trialIntermediate
             };
             this.setState({
@@ -258,6 +273,7 @@ class Intermediate extends React.Component<IntermediateProps, IntermediateState>
 
     render() {
         const { interSource, isLoadconfirmBtn, isFilter } = this.state;
+        const IntermediateEvents = { 'dataZoom': this.intermediateDataZoom };
         return (
             <div>
                 {/* style in para.scss */}
@@ -265,7 +281,7 @@ class Intermediate extends React.Component<IntermediateProps, IntermediateState>
                     {
                         isFilter
                             ?
-                            <span style={{marginRight: 15}}>
+                            <span style={{ marginRight: 15 }}>
                                 <span className="filter-x"># Intermediate result</span>
                                 <input
                                     // placeholder="point"
@@ -306,12 +322,22 @@ class Intermediate extends React.Component<IntermediateProps, IntermediateState>
                         option={interSource}
                         style={{ width: '100%', height: 418, margin: '0 auto' }}
                         notMerge={true} // update now
+                        onEvents={IntermediateEvents}
                     />
                     <div className="yAxis"># Intermediate result</div>
                 </Row>
             </div>
         );
     }
+
+    private intermediateDataZoom = (e: EventMap) => {
+        if (e.batch !== undefined) {
+            this.setState(() => ({
+                startMediaY: (e.batch[0].start !== null ? e.batch[0].start : 0),
+                endMediaY: (e.batch[0].end !== null ? e.batch[0].end : 100)
+            }));
+        }
+    }
 }
 
 export default Intermediate;

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

@@ -275,6 +275,7 @@ class Para extends React.Component<ParaProps, ParaState> {
         parallelAxis.push({
             dim: i,
             name: 'default metric',
+            scale: true,
             nameTextStyle: {
                 fontWeight: 700
             }

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

@@ -586,17 +586,16 @@ const AccuracyColumnConfig: ColumnProps<TableRecord> = {
     dataIndex: 'accuracy',
     width: 120,
     sorter: (a, b, sortOrder) => {
-        if (a.accuracy === undefined) {
-            return sortOrder === 'ascend' ? -1 : 1;
-        } else if (b.accuracy === undefined) {
+        if (a.latestAccuracy === undefined) {
             return sortOrder === 'ascend' ? 1 : -1;
+        } else if (b.latestAccuracy === undefined) {
+            return sortOrder === 'ascend' ? -1 : 1;
         } else {
-            return a.accuracy - b.accuracy;
+            return a.latestAccuracy - b.latestAccuracy;
         }
     },
     render: (text, record) => (
-        // TODO: is this needed?
-        <div>{record.latestAccuracy}</div>
+        <div>{record.formattedLatestAccuracy}</div>
     )
 };
 

src/webui/src/static/function.ts

@@ -186,5 +186,5 @@ function formatAccuracy(accuracy: number): string {
 export {
     convertTime, convertDuration, getFinalResult, getFinal, downFile,
     intermediateGraphOption, killJob, filterByStatus, filterDuration,
-    formatAccuracy, formatTimestamp, metricAccuracy,
+    formatAccuracy, formatTimestamp, metricAccuracy
 };

src/webui/src/static/interface.ts

@@ -24,7 +24,8 @@ interface TableRecord {
     status: string;
     intermediateCount: number;
     accuracy?: number;
-    latestAccuracy: string;  // formatted string
+    latestAccuracy: number | undefined;
+    formattedLatestAccuracy: string; // format (LATEST/FINAL)
 }
 
 interface SearchSpace {
@@ -81,6 +82,7 @@ interface Dimobj {
     axisLabel?: object;
     axisLine?: object;
     nameTextStyle?: object;
+    scale?: boolean;
 }
 
 interface ParaObj {
@@ -179,9 +181,13 @@ interface NNIManagerStatus {
     errors: string[];
 }
 
+interface EventMap {
+    [key: string]: () => void;
+}
+
 export {
     TableObj, TableRecord, Parameters, ExperimentProfile, AccurPoint,
     DetailAccurPoint, TooltipForAccuracy, ParaObj, Dimobj, FinalType,
     TooltipForIntermediate, SearchSpace, Intermedia, MetricDataRecord, TrialJobInfo,
-    NNIManagerStatus,
+    NNIManagerStatus, EventMap
 };

src/webui/src/static/model/trial.ts

@@ -46,6 +46,22 @@ class Trial implements TableObj {
         return this.metricsInitialized && this.finalAcc !== undefined && !isNaN(this.finalAcc);
     }
 
+    get latestAccuracy(): number | undefined {
+        if (this.accuracy !== undefined) {
+            return this.accuracy;
+        } else if (this.intermediates.length > 0) {
+            // TODO: support intermeidate result is dict
+            const temp = this.intermediates[this.intermediates.length - 1];
+            if (temp !== undefined) {
+                return JSON.parse(temp.data);
+            } else {
+                return undefined;
+            }
+        } else {
+            return undefined;
+        }
+    }
+
     /* table obj start */
 
     get tableRecord(): TableRecord {
@@ -62,7 +78,8 @@ class Trial implements TableObj {
             status: this.info.status,
             intermediateCount: this.intermediates.length,
             accuracy: this.finalAcc,
-            latestAccuracy: this.formatLatestAccuracy(),
+            latestAccuracy: this.latestAccuracy,
+            formattedLatestAccuracy: this.formatLatestAccuracy(),
         };
     }
 

test/async_sharing_test/main.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 """
 Test code for weight sharing
 need NFS setup and mounted as `/mnt/nfs/nni`

test/async_sharing_test/simple_tuner.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 """
 SimpleTuner for Weight Sharing
 """

test/cli_test.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.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import sys
 import time
@@ -26,7 +9,7 @@ from utils import GREEN, RED, CLEAR, setup_experiment
 def test_nni_cli():
     import nnicli as nc
 
-    config_file = 'config_test/examples/mnist.test.yml'
+    config_file = 'config_test/examples/mnist-tfv1.test.yml'
 
     try:
         # Sleep here to make sure previous stopped exp has enough time to exit to avoid port conflict

test/config_test.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.
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
 
 import os
 import argparse