Filter prune algo implementation (#1655)

* fpgm pruner pytorch and tensorflow 2.0 implementation

docs/en_US/Compressor/Overview.md

@@ -12,6 +12,7 @@ We have provided two naive compression algorithms and three popular ones for use
 |---|---|
 | [Level Pruner](./Pruner.md#level-pruner) | Pruning the specified ratio on each weight based on absolute values of weights |
 | [AGP Pruner](./Pruner.md#agp-pruner) | Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) [Reference Paper](https://arxiv.org/abs/1710.01878)|
+| [FPGM Pruner](./Pruner.md#fpgm-pruner) | Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration [Reference Paper](https://arxiv.org/pdf/1811.00250.pdf)|
 | [Naive Quantizer](./Quantizer.md#naive-quantizer) |  Quantize weights to default 8 bits |
 | [QAT Quantizer](./Quantizer.md#qat-quantizer) | Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. [Reference Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)|
 | [DoReFa Quantizer](./Quantizer.md#dorefa-quantizer) | DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [Reference Paper](https://arxiv.org/abs/1606.06160)|

docs/en_US/Compressor/Pruner.md

@@ -92,3 +92,49 @@ You can view example for more information
 
 ***
 
+## FPGM Pruner
+FPGM Pruner is an implementation of paper [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://arxiv.org/pdf/1811.00250.pdf)
+
+>Previous works utilized “smaller-norm-less-important” criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small. To solve this problem, we propose a novel filter pruning method, namely Filter Pruning via Geometric Median (FPGM), to compress the model regardless of those two requirements. Unlike previous methods, FPGM compresses CNN models by pruning filters with redundancy, rather than those with “relatively less” importance.
+
+### Usage
+First, you should import pruner and add mask to model.
+
+Tensorflow code
+```python
+from nni.compression.tensorflow import FPGMPruner
+config_list = [{
+    'sparsity': 0.5,
+    'op_types': ['Conv2D']
+}]
+pruner = FPGMPruner(model, config_list)
+pruner.compress()
+```
+PyTorch code
+```python
+from nni.compression.torch import FPGMPruner
+config_list = [{
+    'sparsity': 0.5,
+    'op_types': ['Conv2d']
+}]
+pruner = FPGMPruner(model, config_list)
+pruner.compress()
+```
+Note: FPGM Pruner is used to prune convolutional layers within deep neural networks, therefore the `op_types` field supports only convolutional layers.
+
+Second, you should add code below to update epoch number at beginning of each epoch.
+
+Tensorflow code
+```python
+pruner.update_epoch(epoch, sess)
+```
+PyTorch code
+```python
+pruner.update_epoch(epoch)
+```
+You can view example for more information
+
+#### User configuration for FPGM Pruner
+* **sparsity:** How much percentage of convolutional filters are to be pruned.
+
+***

examples/model_compress/fpgm_tf_mnist.py

+import tensorflow as tf
+from tensorflow import keras
+assert tf.__version__ >= "2.0"
+import numpy as np
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
+from nni.compression.tensorflow import FPGMPruner
+
+def get_data():
+    (X_train_full, y_train_full), _ = keras.datasets.mnist.load_data()
+    X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]
+    y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]
+
+    X_mean = X_train.mean(axis=0, keepdims=True)
+    X_std = X_train.std(axis=0, keepdims=True) + 1e-7
+    X_train = (X_train - X_mean) / X_std
+    X_valid = (X_valid - X_mean) / X_std
+
+    X_train = X_train[..., np.newaxis]
+    X_valid = X_valid[..., np.newaxis]
+
+    return X_train, X_valid, y_train, y_valid
+
+def get_model():
+    model = keras.models.Sequential([
+        Conv2D(filters=32, kernel_size=7, input_shape=[28, 28, 1], activation='relu', padding="SAME"),
+        MaxPooling2D(pool_size=2),
+        Conv2D(filters=64, kernel_size=3, activation='relu', padding="SAME"),
+        MaxPooling2D(pool_size=2),
+        Flatten(),
+        Dense(units=128, activation='relu'),
+        Dropout(0.5),
+        Dense(units=10, activation='softmax'),
+    ])
+    model.compile(loss="sparse_categorical_crossentropy",
+        optimizer=keras.optimizers.SGD(lr=1e-3),
+        metrics=["accuracy"])
+    return model
+
+def main():
+    X_train, X_valid, y_train, y_valid = get_data()
+    model = get_model()
+
+    configure_list = [{
+        'sparsity': 0.5,
+        'op_types': ['Conv2D']
+    }]
+    pruner = FPGMPruner(model, configure_list)
+    pruner.compress()
+
+    update_epoch_callback = keras.callbacks.LambdaCallback(on_epoch_begin=lambda epoch, logs: pruner.update_epoch(epoch))
+
+    model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid), callbacks=[update_epoch_callback])
+
+
+if __name__ == '__main__':
+    main()

examples/model_compress/fpgm_torch_mnist.py

+from nni.compression.torch import FPGMPruner
+import torch
+import torch.nn.functional as F
+from torchvision import datasets, transforms
+
+
+class Mnist(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)
+        self.conv2 = torch.nn.Conv2d(20, 50, 5, 1)
+        self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)
+        self.fc2 = torch.nn.Linear(500, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 4 * 4 * 50)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+    def _get_conv_weight_sparsity(self, conv_layer):
+        num_zero_filters = (conv_layer.weight.data.sum((2,3)) == 0).sum()
+        num_filters = conv_layer.weight.data.size(0) * conv_layer.weight.data.size(1)
+        return num_zero_filters, num_filters, float(num_zero_filters)/num_filters
+
+    def print_conv_filter_sparsity(self):
+        conv1_data = self._get_conv_weight_sparsity(self.conv1)
+        conv2_data = self._get_conv_weight_sparsity(self.conv2)
+        print('conv1: num zero filters: {}, num filters: {}, sparsity: {:.4f}'.format(conv1_data[0], conv1_data[1], conv1_data[2]))
+        print('conv2: num zero filters: {}, num filters: {}, sparsity: {:.4f}'.format(conv2_data[0], conv2_data[1], conv2_data[2]))
+
+def train(model, device, train_loader, optimizer):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        if batch_idx % 100 == 0:
+            print('{:2.0f}%  Loss {}'.format(100 * batch_idx / len(train_loader), loss.item()))
+            model.print_conv_filter_sparsity()
+        loss.backward()
+        optimizer.step()
+
+def test(model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+    test_loss /= len(test_loader.dataset)
+
+    print('Loss: {}  Accuracy: {}%)\n'.format(
+        test_loss, 100 * correct / len(test_loader.dataset)))
+
+
+def main():
+    torch.manual_seed(0)
+    device = torch.device('cpu')
+
+    trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+    train_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('data', train=True, download=True, transform=trans),
+        batch_size=64, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST('data', train=False, transform=trans),
+        batch_size=1000, shuffle=True)
+
+    model = Mnist()
+    model.print_conv_filter_sparsity()
+
+    '''you can change this to LevelPruner to implement it
+    pruner = LevelPruner(configure_list)
+    '''
+    configure_list = [{
+        'sparsity': 0.5,
+        'op_types': ['Conv2d']
+    }]
+
+    pruner = FPGMPruner(model, configure_list)
+    pruner.compress()
+
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
+    for epoch in range(10):
+        pruner.update_epoch(epoch)
+        print('# Epoch {} #'.format(epoch))
+        train(model, device, train_loader, optimizer)
+        test(model, device, test_loader)
+
+
+if __name__ == '__main__':
+    main()

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

 import logging
+import numpy as np
 import tensorflow as tf
 from .compressor import Pruner
 
-__all__ = ['LevelPruner', 'AGP_Pruner']
+__all__ = ['LevelPruner', 'AGP_Pruner', 'FPGMPruner']
 
 _logger = logging.getLogger(__name__)
 
@@ -98,3 +99,104 @@ class AGP_Pruner(Pruner):
         sess.run(tf.assign(self.now_epoch, int(epoch)))
         for k in self.if_init_list:
             self.if_init_list[k] = True
+
+class FPGMPruner(Pruner):
+    """
+    A filter pruner via geometric median.
+    "Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration",
+    https://arxiv.org/pdf/1811.00250.pdf
+    """
+
+    def __init__(self, model, config_list):
+        """
+        Parameters
+        ----------
+        model : pytorch model
+            the model user wants to compress
+        config_list: list
+            support key for each list item:
+                - sparsity: percentage of convolutional filters to be pruned.
+        """
+        super().__init__(model, config_list)
+        self.mask_dict = {}
+        self.assign_handler = []
+        self.epoch_pruned_layers = set()
+
+    def calc_mask(self, layer, config):
+        """
+        Supports Conv1D, Conv2D
+        filter dimensions for Conv1D:
+        LEN: filter length
+        IN: number of input channel
+        OUT: number of output channel
+
+        filter dimensions for Conv2D:
+        H: filter height
+        W: filter width
+        IN: number of input channel
+        OUT: number of output channel
+
+        Parameters
+        ----------
+        layer : LayerInfo
+            calculate mask for `layer`'s weight
+        config : dict
+            the configuration for generating the mask
+        """
+
+        weight = layer.weight
+        op_type = layer.type
+        op_name = layer.name
+        assert 0 <= config.get('sparsity') < 1
+        assert op_type in ['Conv1D', 'Conv2D']
+        assert op_type in config['op_types']
+
+        if layer.name in self.epoch_pruned_layers:
+            assert layer.name in self.mask_dict
+            return self.mask_dict.get(layer.name)
+
+        try:
+            weight = tf.stop_gradient(tf.transpose(weight, [2, 3, 0, 1]))
+            masks = np.ones(weight.shape)
+
+            num_kernels = weight.shape[0] * weight.shape[1]
+            num_prune = int(num_kernels * config.get('sparsity'))
+            if num_kernels < 2 or num_prune < 1:
+                return masks
+            min_gm_idx = self._get_min_gm_kernel_idx(weight, num_prune)
+            for idx in min_gm_idx:
+                masks[tuple(idx)] = 0.
+        finally:
+            masks = np.transpose(masks, [2, 3, 0, 1])
+            masks = tf.Variable(masks)
+            self.mask_dict.update({op_name: masks})
+            self.epoch_pruned_layers.add(layer.name)
+
+        return masks
+
+    def _get_min_gm_kernel_idx(self, weight, n):
+        assert len(weight.shape) >= 3
+        assert weight.shape[0] * weight.shape[1] > 2
+
+        dist_list, idx_list = [], []
+        for in_i in range(weight.shape[0]):
+            for out_i in range(weight.shape[1]):
+                dist_sum = self._get_distance_sum(weight, in_i, out_i)
+                dist_list.append(dist_sum)
+                idx_list.append([in_i, out_i])
+        dist_tensor = tf.convert_to_tensor(dist_list)
+        idx_tensor = tf.constant(idx_list)
+
+        _, idx = tf.math.top_k(dist_tensor, k=n)
+        return tf.gather(idx_tensor, idx)
+
+    def _get_distance_sum(self, weight, in_idx, out_idx):
+        w = tf.reshape(weight, (-1, weight.shape[-2], weight.shape[-1]))
+        anchor_w = tf.tile(tf.expand_dims(weight[in_idx, out_idx], 0), [w.shape[0], 1, 1])
+        x = w - anchor_w
+        x = tf.math.reduce_sum((x*x), (-2, -1))
+        x = tf.math.sqrt(x)
+        return tf.math.reduce_sum(x)
+
+    def update_epoch(self, epoch):
+        self.epoch_pruned_layers = set()

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

 import logging
 import tensorflow as tf
 from . import default_layers
+tf.config.experimental_run_functions_eagerly(True)
 
 _logger = logging.getLogger(__name__)
 
 
 class LayerInfo:
-    def __init__(self, op, weight, weight_op):
-        self.op = op
-        self.name = op.name
-        self.type = op.type
-        self.weight = weight
-        self.weight_op = weight_op
-
+    def __init__(self, keras_layer):
+        self.keras_layer = keras_layer
+        self.name = keras_layer.name
+        self.type = default_layers.get_op_type(type(keras_layer))
+        self.weight_index = default_layers.get_weight_index(self.type)
+        if self.weight_index is not None:
+            self.weight = keras_layer.weights[self.weight_index]
+        self._call = None
 
 class Compressor:
     """
@@ -25,7 +27,7 @@ class Compressor:
 
         Parameters
         ----------
-        model : pytorch model
+        model : keras model
             the model user wants to compress
         config_list : list
             the configurations that users specify for compression
@@ -34,6 +36,21 @@ class Compressor:
         self.config_list = config_list
         self.modules_to_compress = []
 
+    def detect_modules_to_compress(self):
+        """
+        detect all modules should be compressed, and save the result in `self.modules_to_compress`.
+
+        The model will be instrumented and user should never edit it after calling this method.
+        """
+        if self.modules_to_compress is None:
+            self.modules_to_compress = []
+            for keras_layer in self.bound_model.layers:
+                layer = LayerInfo(keras_layer)
+                config = self.select_config(layer)
+                if config is not None:
+                    self.modules_to_compress.append((layer, config))
+        return self.modules_to_compress
+
     def compress(self):
         """
         Compress the model with algorithm implemented by subclass.
@@ -41,19 +58,9 @@ class Compressor:
         The model will be instrumented and user should never edit it after calling this method.
         `self.modules_to_compress` records all the to-be-compressed layers
         """
-        for op in self.bound_model.get_operations():
-            weight_index = _detect_weight_index(op)
-            if weight_index is None:
-                _logger.warning('Failed to detect weight for layer %s', op.name)
-                return
-            weight_op = op.inputs[weight_index].op
-            weight = weight_op.inputs[0]
-
-            layer = LayerInfo(op, weight, weight_op)
-            config = self.select_config(layer)
-            if config is not None:
+        modules_to_compress = self.detect_modules_to_compress()
+        for layer, config in modules_to_compress:
             self._instrument_layer(layer, config)
-                self.modules_to_compress.append((layer, config))
         return self.bound_model
 
     def get_modules_to_compress(self):
@@ -74,7 +81,7 @@ class Compressor:
 
         Parameters
         ----------
-        layer : LayerInfo
+        layer: LayerInfo
             one layer
 
         Returns
@@ -84,11 +91,12 @@ class Compressor:
             not be compressed
         """
         ret = None
+        if layer.type is None:
+            return None
         for config in self.config_list:
-            op_types = config.get('op_types')
-            if op_types == 'default':
-                op_types = default_layers.op_weight_index.keys()
-            if op_types and layer.type not in op_types:
+            config = config.copy()
+            config['op_types'] = self._expand_config_op_types(config)
+            if layer.type not in config['op_types']:
                 continue
             if config.get('op_names') and layer.name not in config['op_names']:
                 continue
@@ -97,7 +105,7 @@ class Compressor:
             return None
         return ret
 
-    def update_epoch(self, epoch, sess):
+    def update_epoch(self, epoch):
         """
         If user want to update model every epoch, user can override this method.
         This method should be called at the beginning of each epoch
@@ -108,7 +116,7 @@ class Compressor:
             the current epoch number
         """
 
-    def step(self, sess):
+    def step(self):
         """
         If user want to update mask every step, user can override this method
         """
@@ -127,6 +135,18 @@ class Compressor:
         """
         raise NotImplementedError()
 
+    def _expand_config_op_types(self, config):
+        if config is None:
+            return []
+        op_types = []
+
+        for op_type in config.get('op_types', []):
+            if op_type == 'default':
+                op_types.extend(default_layers.default_layers)
+            else:
+                op_types.append(op_type)
+        return op_types
+
 
 class Pruner(Compressor):
     """
@@ -160,10 +180,17 @@ class Pruner(Compressor):
         config : dict
             the configuration for generating the mask
         """
+        layer._call = layer.keras_layer.call
+
+        def new_call(*inputs):
+            weights = [x.numpy() for x in layer.keras_layer.weights]
             mask = self.calc_mask(layer, config)
-        new_weight = layer.weight * mask
-        tf.contrib.graph_editor.swap_outputs(layer.weight_op, new_weight.op)
+            weights[layer.weight_index] = weights[layer.weight_index] * mask
+            layer.keras_layer.set_weights(weights)
+            ret = layer._call(*inputs)
+            return ret
 
+        layer.keras_layer.call = new_call
 
 class Quantizer(Compressor):
     """
@@ -172,23 +199,3 @@ class Quantizer(Compressor):
 
     def quantize_weight(self, weight, config, op, op_type, op_name):
         raise NotImplementedError("Quantizer must overload quantize_weight()")
-
-    def _instrument_layer(self, layer, config):
-        weight_index = _detect_weight_index(layer)
-        if weight_index is None:
-            _logger.warning('Failed to detect weight for layer %s', layer.name)
-            return
-        weight_op = layer.op.inputs[weight_index].op
-        weight = weight_op.inputs[0]
-        new_weight = self.quantize_weight(weight, config, op=layer.op, op_type=layer.type, op_name=layer.name)
-        tf.contrib.graph_editor.swap_outputs(weight_op, new_weight.op)
-
-
-def _detect_weight_index(layer):
-    index = default_layers.op_weight_index.get(layer.type)
-    if index is not None:
-        return index
-    weight_indices = [i for i, op in enumerate(layer.inputs) if op.name.endswith('Variable/read')]
-    if len(weight_indices) == 1:
-        return weight_indices[0]
-    return None

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

-op_weight_index = {
-    'Conv2D': None,
-    'Conv3D': None,
-    'DepthwiseConv2dNative': None,
+from tensorflow import keras
 
-    'Mul': None,
-    'MatMul': None,
+supported_layers = {
+    keras.layers.Conv1D: ('Conv1D', 0),
+    keras.layers.Conv2D: ('Conv2D', 0),
+    keras.layers.Conv2DTranspose: ('Conv2DTranspose', 0),
+    keras.layers.Conv3D: ('Conv3D', 0),
+    keras.layers.Conv3DTranspose: ('Conv3DTranspose', 0),
+    keras.layers.ConvLSTM2D: ('ConvLSTM2D', 0),
+    keras.layers.Dense: ('Dense', 0),
+    keras.layers.Embedding: ('Embedding', 0),
+    keras.layers.GRU: ('GRU', 0),
+    keras.layers.LSTM: ('LSTM', 0),
 }
+
+default_layers = [x[0] for x in supported_layers.values()]
+
+def get_op_type(layer_type):
+    if layer_type in supported_layers:
+        return supported_layers[layer_type][0]
+    else:
+        return None
+
+def get_weight_index(op_type):
+    for k in supported_layers:
+        if supported_layers[k][0] == op_type:
+            return supported_layers[k][1]
+    return None

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']
+__all__ = ['LevelPruner', 'AGP_Pruner', 'FPGMPruner']
 
 logger = logging.getLogger('torch pruner')
 
@@ -106,3 +106,125 @@ class AGP_Pruner(Pruner):
             self.now_epoch = epoch
             for k in self.if_init_list:
                 self.if_init_list[k] = True
+
+class FPGMPruner(Pruner):
+    """
+    A filter pruner via geometric median.
+    "Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration",
+    https://arxiv.org/pdf/1811.00250.pdf
+    """
+
+    def __init__(self, model, config_list):
+        """
+        Parameters
+        ----------
+        model : pytorch model
+            the model user wants to compress
+        config_list: list
+            support key for each list item:
+                - sparsity: percentage of convolutional filters to be pruned.
+        """
+        super().__init__(model, config_list)
+        self.mask_dict = {}
+        self.epoch_pruned_layers = set()
+
+    def calc_mask(self, layer, config):
+        """
+        Supports Conv1d, Conv2d
+        filter dimensions for Conv1d:
+        OUT: number of output channel
+        IN: number of input channel
+        LEN: filter length
+
+        filter dimensions for Conv2d:
+        OUT: number of output channel
+        IN: number of input channel
+        H: filter height
+        W: filter width
+
+        Parameters
+        ----------
+        layer : LayerInfo
+            calculate mask for `layer`'s weight
+        config : dict
+            the configuration for generating the mask
+        """
+        weight = layer.module.weight.data
+        assert 0 <= config.get('sparsity') < 1
+        assert layer.type in ['Conv1d', 'Conv2d']
+        assert layer.type in config['op_types']
+
+        if layer.name in self.epoch_pruned_layers:
+            assert layer.name in self.mask_dict
+            return self.mask_dict.get(layer.name)
+
+        masks = torch.ones(weight.size()).type_as(weight)
+
+        try:
+            num_kernels = weight.size(0) * weight.size(1)
+            num_prune = int(num_kernels * config.get('sparsity'))
+            if num_kernels < 2 or num_prune < 1:
+                return masks
+            min_gm_idx = self._get_min_gm_kernel_idx(weight, num_prune)
+            for idx in min_gm_idx:
+                masks[idx] = 0.
+        finally:
+            self.mask_dict.update({layer.name: masks})
+            self.epoch_pruned_layers.add(layer.name)
+
+        return masks
+
+    def _get_min_gm_kernel_idx(self, weight, n):
+        assert len(weight.size()) in [3, 4]
+
+        dist_list = []
+        for out_i in range(weight.size(0)):
+            for in_i in range(weight.size(1)):
+                dist_sum = self._get_distance_sum(weight, out_i, in_i)
+                dist_list.append((dist_sum, (out_i, in_i)))
+        min_gm_kernels = sorted(dist_list, key=lambda x: x[0])[:n]
+        return [x[1] for x in min_gm_kernels]
+
+    def _get_distance_sum(self, weight, out_idx, in_idx):
+        """
+        Calculate the total distance between a specified filter (by out_idex and in_idx) and
+        all other filters.
+        Optimized verision of following naive implementation:
+        def _get_distance_sum(self, weight, in_idx, out_idx):
+            w = weight.view(-1, weight.size(-2), weight.size(-1))
+            dist_sum = 0.
+            for k in w:
+                dist_sum += torch.dist(k, weight[in_idx, out_idx], p=2)
+            return dist_sum
+
+        Parameters
+        ----------
+        weight: Tensor
+            convolutional filter weight
+        out_idx: int
+            output channel index of specified filter, this method calculates the total distance
+            between this specified filter and all other filters.
+        in_idx: int
+            input channel index of specified filter
+
+        Returns
+        -------
+        float32
+            The total distance
+        """
+        logger.debug('weight size: %s', weight.size())
+        if len(weight.size()) == 4: # Conv2d
+            w = weight.view(-1, weight.size(-2), weight.size(-1))
+            anchor_w = weight[out_idx, in_idx].unsqueeze(0).expand(w.size(0), w.size(1), w.size(2))
+        elif len(weight.size()) == 3: # Conv1d
+            w = weight.view(-1, weight.size(-1))
+            anchor_w = weight[out_idx, in_idx].unsqueeze(0).expand(w.size(0), w.size(1))
+        else:
+            raise RuntimeError('unsupported layer type')
+        x = w - anchor_w
+        x = (x*x).sum((-2, -1))
+        x = torch.sqrt(x)
+        return x.sum()
+
+    def update_epoch(self, epoch):
+        self.epoch_pruned_layers = set()

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

@@ -91,6 +91,7 @@ class Compressor:
         """
         ret = None
         for config in self.config_list:
+            config = config.copy()
             config['op_types'] = self._expand_config_op_types(config)
             if layer.type not in config['op_types']:
                 continue

src/sdk/pynni/tests/test_compressor.py

@@ -2,81 +2,26 @@ from unittest import TestCase, main
 import tensorflow as tf
 import torch
 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))
-
-
-def bias_variable(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')
-
-
-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')
-
-
-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')
-        keep_prob = tf.placeholder(tf.float32, name='keep_prob')
-
-        self.images = images
-        self.labels = labels
-        self.keep_prob = keep_prob
-
-        self.train_step = None
-        self.accuracy = None
-
-        self.w1 = None
-        self.b1 = None
-        self.fcw1 = None
-        self.cross = None
-        with tf.name_scope('reshape'):
-            x_image = tf.reshape(images, [-1, 28, 28, 1])
-        with tf.name_scope('conv1'):
-            w_conv1 = weight_variable([5, 5, 1, 32])
-            self.w1 = w_conv1
-            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])
-            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])
-            self.fcw1 = w_fc1
-            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])
-            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))
-            self.cross = cross_entropy
-        with tf.name_scope('adam_optimizer'):
-            self.train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy)
-        with tf.name_scope('accuracy'):
-            correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(labels, 1))
-            self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-
+if tf.__version__ >= '2.0':
+    import nni.compression.tensorflow as tf_compressor
+
+def get_tf_mnist_model():
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Conv2D(filters=32, kernel_size=7, input_shape=[28, 28, 1], activation='relu', padding="SAME"),
+        tf.keras.layers.MaxPooling2D(pool_size=2),
+        tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='relu', padding="SAME"),
+        tf.keras.layers.MaxPooling2D(pool_size=2),
+        tf.keras.layers.Flatten(),
+        tf.keras.layers.Dense(units=128, activation='relu'),
+        tf.keras.layers.Dropout(0.5),
+        tf.keras.layers.Dense(units=10, activation='softmax'),
+    ])
+    model.compile(loss="sparse_categorical_crossentropy",
+        optimizer=tf.keras.optimizers.SGD(lr=1e-3),
+        metrics=["accuracy"])
+    return model
 
 class TorchMnist(torch.nn.Module):
     def __init__(self):
@@ -96,22 +41,23 @@ class TorchMnist(torch.nn.Module):
         x = self.fc2(x)
         return F.log_softmax(x, dim=1)
 
+def tf2(func):
+    def test_tf2_func(self):
+        if tf.__version__ >= '2.0':
+            func()
+    return test_tf2_func
 
 class CompressorTestCase(TestCase):
-    def test_tf_pruner(self):
-        model = TfMnist()
-        configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
-        tf_compressor.LevelPruner(tf.get_default_graph(), configure_list).compress()
-
-    def test_tf_quantizer(self):
-        model = TfMnist()
-        tf_compressor.NaiveQuantizer(tf.get_default_graph(), [{'op_types': ['default']}]).compress()
-
     def test_torch_pruner(self):
         model = TorchMnist()
         configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
         torch_compressor.LevelPruner(model, configure_list).compress()
 
+    def test_torch_fpgm_pruner(self):
+        model = TorchMnist()
+        configure_list = [{'sparsity': 0.5, 'op_types': ['Conv2d']}]
+        torch_compressor.FPGMPruner(model, configure_list).compress()
+
     def test_torch_quantizer(self):
         model = TorchMnist()
         configure_list = [{
@@ -123,6 +69,20 @@ class CompressorTestCase(TestCase):
         }]
         torch_compressor.NaiveQuantizer(model, configure_list).compress()
 
+    @tf2
+    def test_tf_pruner(self):
+        configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
+        tf_compressor.LevelPruner(get_tf_mnist_model(), configure_list).compress()
+
+    @tf2
+    def test_tf_quantizer(self):
+        tf_compressor.NaiveQuantizer(get_tf_mnist_model(), [{'op_types': ['default']}]).compress()
+
+    @tf2
+    def test_tf_fpgm_pruner(self):
+        configure_list = [{'sparsity': 0.5, 'op_types': ['Conv2D']}]
+        tf_compressor.FPGMPruner(get_tf_mnist_model(), configure_list).compress()
+
 
 if __name__ == '__main__':
     main()