support classic nas mode: each chosen arch as a separate trial job

examples/nas/classic_nas/config_nas.yml

@@ -0,0 +1,17 @@
+authorName: default
+experimentName: example_mnist
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: NNI_AUTO_GEN
+useAnnotation: False
+tuner:
+  codeDir: ../../tuners/random_nas_tuner
+  classFileName: random_nas_tuner.py
+  className: RandomNASTuner
+trial:
+  command: python3 mnist.py
+  codeDir: .
+  gpuNum: 0

examples/nas/classic_nas/config_ppo.yml

@@ -0,0 +1,17 @@
+authorName: default
+experimentName: example_mnist
+trialConcurrency: 1
+maxExecDuration: 100h
+maxTrialNum: 1000
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: NNI_AUTO_GEN
+useAnnotation: False
+tuner:
+  builtinTunerName: PPOTuner
+  classArgs:
+    optimize_mode: maximize
+trial:
+  command: python3 mnist.py
+  codeDir: .
+  gpuNum: 0

examples/nas/classic_nas/mnist.py

@@ -0,0 +1,181 @@
+"""
+A deep MNIST classifier using convolutional layers.
+
+This file is a modification of the official pytorch mnist example:
+https://github.com/pytorch/examples/blob/master/mnist/main.py
+"""
+
+import os
+import argparse
+import logging
+import nni
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+
+from nni.nas.pytorch.mutables import LayerChoice, InputChoice
+from nni.nas.pytorch.classic_nas import get_apply_next_architecture
+
+
+logger = logging.getLogger('mnist_AutoML')
+
+
+class Net(nn.Module):
+    def __init__(self, hidden_size):
+        super(Net, self).__init__()
+        # two options of conv1
+        self.conv1 = LayerChoice([nn.Conv2d(1, 20, 5, 1),
+                                  nn.Conv2d(1, 20, 3, 1)],
+                                 key='first_conv')
+        # two options of mid_conv
+        self.mid_conv = LayerChoice([nn.Conv2d(20, 20, 3, 1, padding=1),
+                                     nn.Conv2d(20, 20, 5, 1, padding=2)],
+                                    key='mid_conv')
+        self.conv2 = nn.Conv2d(20, 50, 5, 1)
+        self.fc1 = nn.Linear(4*4*50, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, 10)
+        # skip connection over mid_conv
+        self.input_switch = InputChoice(choose_from=['', 'mid_conv'],
+                                        n_chosen=1,
+                                        key='skip')
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        old_x = x
+        x = F.relu(self.mid_conv(x))
+        zero_x = torch.zeros_like(old_x).float()
+        skip_x = self.input_switch([zero_x, old_x])
+        x = torch.add(x, skip_x)
+        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 train(args, model, device, train_loader, optimizer, epoch):
+    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)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args['log_interval'] == 0:
+            logger.info('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+
+def test(args, 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)
+            # sum up batch loss
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            # get the index of the max log-probability
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+
+    accuracy = 100. * correct / len(test_loader.dataset)
+
+    logger.info('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset), accuracy))
+
+    return accuracy
+
+
+def main(args):
+    use_cuda = not args['no_cuda'] and torch.cuda.is_available()
+
+    torch.manual_seed(args['seed'])
+
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+
+    #data_dir = os.path.join(args['data_dir'], nni.get_trial_id())
+    data_dir = os.path.join(args['data_dir'], 'data')
+
+    train_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(data_dir, train=True, download=True,
+                       transform=transforms.Compose([
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.1307,), (0.3081,))
+                       ])),
+        batch_size=args['batch_size'], shuffle=True, **kwargs)
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(data_dir, train=False, transform=transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))
+        ])),
+        batch_size=1000, shuffle=True, **kwargs)
+
+    hidden_size = args['hidden_size']
+
+    model = Net(hidden_size=hidden_size).to(device)
+    get_apply_next_architecture(model)
+    optimizer = optim.SGD(model.parameters(), lr=args['lr'],
+                          momentum=args['momentum'])
+
+    for epoch in range(1, args['epochs'] + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test_acc = test(args, model, device, test_loader)
+
+        if epoch < args['epochs']:
+            # report intermediate result
+            nni.report_intermediate_result(test_acc)
+            logger.debug('test accuracy %g', test_acc)
+            logger.debug('Pipe send intermediate result done.')
+        else:
+            # report final result
+            nni.report_final_result(test_acc)
+            logger.debug('Final result is %g', test_acc)
+            logger.debug('Send final result done.')
+
+
+def get_params():
+    # Training settings
+    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+    parser.add_argument("--data_dir", type=str,
+                        default='/tmp/tensorflow/mnist/input_data', help="data directory")
+    parser.add_argument('--batch_size', type=int, default=64, metavar='N',
+                        help='input batch size for training (default: 64)')
+    parser.add_argument("--hidden_size", type=int, default=512, metavar='N',
+                        help='hidden layer size (default: 512)')
+    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
+                        help='learning rate (default: 0.01)')
+    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
+                        help='SGD momentum (default: 0.5)')
+    parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                        help='number of epochs to train (default: 10)')
+    parser.add_argument('--seed', type=int, default=1, metavar='S',
+                        help='random seed (default: 1)')
+    parser.add_argument('--no_cuda', action='store_true', default=False,
+                        help='disables CUDA training')
+    parser.add_argument('--log_interval', type=int, default=1000, metavar='N',
+                        help='how many batches to wait before logging training status')
+
+
+    args, _ = parser.parse_known_args()
+    return args
+
+
+if __name__ == '__main__':
+    try:
+        params = vars(get_params())
+        main(params)
+    except Exception as exception:
+        logger.exception(exception)
+        raise

examples/tuners/random_nas_tuner/random_nas_tuner.py

@@ -6,36 +6,27 @@
 def random_archi_generator(nas_ss, random_state):
     '''random
     '''
-    chosen_archi = {}
-    for block_name, block_value in nas_ss.items():
-        assert block_value['_type'] == "mutable_layer", \
-            "Random NAS Tuner only receives NAS search space whose _type is 'mutable_layer'"
-        block = block_value['_value']
-        tmp_block = {}
-        for layer_name, layer in block.items():
-            tmp_layer = {}
-            for key, value in layer.items():
-                if key == 'layer_choice':
-                    index = random_state.randint(len(value))
-                    tmp_layer['chosen_layer'] = value[index]
-                elif key == 'optional_inputs':
-                    tmp_layer['chosen_inputs'] = []
-                    if layer['optional_inputs']:
-                        if isinstance(layer['optional_input_size'], int):
-                            choice_num = layer['optional_input_size']
-                        else:
-                            choice_range = layer['optional_input_size']
-                            choice_num = random_state.randint(choice_range[0], choice_range[1] + 1)
-                        for _ in range(choice_num):
-                            index = random_state.randint(len(layer['optional_inputs']))
-                            tmp_layer['chosen_inputs'].append(layer['optional_inputs'][index])
-                elif key == 'optional_input_size':
-                    pass
-                else:
-                    raise ValueError('Unknown field %s in layer %s of block %s' % (key, layer_name, block_name))
-            tmp_block[layer_name] = tmp_layer
-        chosen_archi[block_name] = tmp_block
-    return chosen_archi
+    chosen_arch = {}
+    for key, val in nas_ss.items():
+        assert val['_type'] in ['layer_choice', 'input_choice'], \
+            "Random NAS Tuner only receives NAS search space whose _type is 'layer_choice' or 'input_choice'"
+        if val['_type'] == 'layer_choice':
+            choices = val['_value']
+            index = random_state.randint(len(choices))
+            chosen_arch[key] = {'_value': choices[index], '_idx': index}
+        elif val['_type'] == 'input_choice':
+            choices = val['_value']['candidates']
+            n_chosen = val['_value']['n_chosen']
+            chosen = []
+            idxs = []
+            for _ in range(n_chosen):
+                index = random_state.randint(len(choices))
+                chosen.append(choices[index])
+                idxs.append(index)
+            chosen_arch[key] = {'_value': chosen, '_idx': idxs}
+        else:
+            raise ValueError('Unknown key %s and value %s' % (key, val))
+    return chosen_arch
 
 
 class RandomNASTuner(Tuner):

src/sdk/pynni/nni/nas/pytorch/classic_nas/__init__.py

@@ -0,0 +1 @@
+from .mutator import get_apply_next_architecture