Merge branch 'master' into v2.0-merge

examples/model_compress/amc/README.md

+# AMCPruner Example
+This example shows us how to use AMCPruner example.
+
+## Step 1: train a model for pruning
+Run following command to train a mobilenetv2 model:
+```bash
+python3 amc_train.py --model_type mobilenetv2 --n_epoch 50
+```
+Once finished, saved checkpoint file can be found at:
+```
+logs/mobilenetv2_cifar10_train-run1/ckpt.best.pth
+```
+
+## Pruning with AMCPruner
+Run following command to prune the trained model:
+```bash
+python3 amc_search.py --model_type mobilenetv2 --ckpt logs/mobilenetv2_cifar10_train-run1/ckpt.best.pth
+```
+Once finished, pruned model and mask can be found at:
+```
+logs/mobilenetv2_cifar10_r0.5_search-run2
+```
+
+## Finetune pruned model
+Run `amc_train.py` again with `--ckpt` and `--mask` to speedup and finetune the pruned model:
+```bash
+python3 amc_train.py --model_type mobilenetv2 --ckpt logs/mobilenetv2_cifar10_r0.5_search-run2/best_model.pth --mask logs/mobilenetv2_cifar10_r0.5_search-run2/best_mask.pth --n_epoch 100
+```

examples/model_compress/amc/amc_search.py

@@ -20,7 +20,7 @@ def parse_args():
         help='model to prune')
     parser.add_argument('--dataset', default='cifar10', type=str, choices=['cifar10', 'imagenet'], help='dataset to use (cifar/imagenet)')
     parser.add_argument('--batch_size', default=50, type=int, help='number of data batch size')
-    parser.add_argument('--data_root', default='./cifar10', type=str, help='dataset path')
+    parser.add_argument('--data_root', default='./data', type=str, help='dataset path')
     parser.add_argument('--flops_ratio', default=0.5, type=float, help='target flops ratio to preserve of the model')
     parser.add_argument('--lbound', default=0.2, type=float, help='minimum sparsity')
     parser.add_argument('--rbound', default=1., type=float, help='maximum sparsity')

examples/model_compress/amc/amc_train.py

@@ -13,6 +13,7 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 from tensorboardX import SummaryWriter
+from torchvision.models import resnet
 
 from nni.compression.torch.pruning.amc.lib.net_measure import measure_model
 from nni.compression.torch.pruning.amc.lib.utils import get_output_folder
@@ -27,7 +28,9 @@ from mobilenet_v2 import MobileNetV2
 
 def parse_args():
     parser = argparse.ArgumentParser(description='AMC train / fine-tune script')
-    parser.add_argument('--model_type', default='mobilenet', type=str, help='name of the model to train')
+    parser.add_argument('--model_type', default='mobilenet', type=str,
+        choices=['mobilenet', 'mobilenetv2', 'resnet18', 'resnet34', 'resnet50'],
+        help='name of the model to train')
     parser.add_argument('--dataset', default='cifar10', type=str, help='name of the dataset to train')
     parser.add_argument('--lr', default=0.05, type=float, help='learning rate')
     parser.add_argument('--n_gpu', default=4, type=int, help='number of GPUs to use')
@@ -62,17 +65,21 @@ def get_model(args):
         net = MobileNet(n_class=n_class)
     elif args.model_type == 'mobilenetv2':
         net = MobileNetV2(n_class=n_class)
+    elif args.model_type.startswith('resnet'):
+        net = resnet.__dict__[args.model_type](pretrained=True)
+        in_features = net.fc.in_features
+        net.fc = nn.Linear(in_features, n_class)
     else:
         raise NotImplementedError
 
     if args.ckpt_path is not None:
         # the checkpoint can be state_dict exported by amc_search.py or saved by amc_train.py
         print('=> Loading checkpoint {} ..'.format(args.ckpt_path))
-        net.load_state_dict(torch.load(args.ckpt_path))
+        net.load_state_dict(torch.load(args.ckpt_path, torch.device('cpu')))
         if args.mask_path is not None:
             SZ = 224 if args.dataset == 'imagenet' else 32
             data = torch.randn(2, 3, SZ, SZ)
-            ms = ModelSpeedup(net, data, args.mask_path)
+            ms = ModelSpeedup(net, data, args.mask_path, torch.device('cpu'))
             ms.speedup_model()
 
     net.to(args.device)
@@ -179,11 +186,11 @@ def adjust_learning_rate(optimizer, epoch):
     return lr
 
 def save_checkpoint(state, is_best, checkpoint_dir='.'):
-    filename = os.path.join(checkpoint_dir, 'ckpt.pth.tar')
+    filename = os.path.join(checkpoint_dir, 'ckpt.pth')
     print('=> Saving checkpoint to {}'.format(filename))
     torch.save(state, filename)
     if is_best:
-        shutil.copyfile(filename, filename.replace('.pth.tar', '.best.pth.tar'))
+        shutil.copyfile(filename, filename.replace('.pth', '.best.pth'))
 
 if __name__ == '__main__':
     args = parse_args()

examples/nas/naive-tf/train.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import tensorflow as tf
 from tensorflow.keras import Model
 from tensorflow.keras.layers import (AveragePooling2D, BatchNormalization, Conv2D, Dense, MaxPool2D)
@@ -7,8 +10,6 @@ from tensorflow.keras.optimizers import SGD
 from nni.nas.tensorflow.mutables import LayerChoice, InputChoice
 from nni.nas.tensorflow.enas import EnasTrainer
 
-tf.get_logger().setLevel('ERROR')
-
 
 class Net(Model):
     def __init__(self):
@@ -53,35 +54,36 @@ class Net(Model):
         return x
 
 
-def accuracy(output, target):
-    bs = target.shape[0]
-    predicted = tf.cast(tf.argmax(output, 1), target.dtype)
-    target = tf.reshape(target, [-1])
-    return sum(tf.cast(predicted == target, tf.float32)) / bs
+def accuracy(truth, logits):
+    truth = tf.reshape(truth, -1)
+    predicted = tf.cast(tf.math.argmax(logits, axis=1), truth.dtype)
+    equal = tf.cast(predicted == truth, tf.int32)
+    return tf.math.reduce_sum(equal).numpy() / equal.shape[0]
+
+def accuracy_metrics(truth, logits):
+    acc = accuracy(truth, logits)
+    return {'accuracy': acc}
 
 
 if __name__ == '__main__':
     cifar10 = tf.keras.datasets.cifar10
-    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
-    x_train, x_test = x_train / 255.0, x_test / 255.0
-    split = int(len(x_train) * 0.9)
-    dataset_train = tf.data.Dataset.from_tensor_slices((x_train[:split], y_train[:split])).batch(64)
-    dataset_valid = tf.data.Dataset.from_tensor_slices((x_train[split:], y_train[split:])).batch(64)
-    dataset_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(64)
+    (x_train, y_train), (x_valid, y_valid) = cifar10.load_data()
+    x_train, x_valid = x_train / 255.0, x_valid / 255.0
+    train_set = (x_train, y_train)
+    valid_set = (x_valid, y_valid)
 
     net = Net()
+
     trainer = EnasTrainer(
         net,
-        loss=SparseCategoricalCrossentropy(reduction=Reduction.SUM),
-        metrics=accuracy,
+        loss=SparseCategoricalCrossentropy(from_logits=True, reduction=Reduction.NONE),
+        metrics=accuracy_metrics,
         reward_function=accuracy,
         optimizer=SGD(learning_rate=0.001, momentum=0.9),
         batch_size=64,
         num_epochs=2,
-        dataset_train=dataset_train,
-        dataset_valid=dataset_valid,
-        dataset_test=dataset_test
+        dataset_train=train_set,
+        dataset_valid=valid_set
     )
 
     trainer.train()
-    #trainer.export('checkpoint')

examples/nas/spos/supernet.py

@@ -45,6 +45,7 @@ if __name__ == "__main__":
     torch.backends.cudnn.deterministic = True
 
     model = ShuffleNetV2OneShot()
+    flops_func = model.get_candidate_flops
     if args.load_checkpoint:
         if not args.spos_preprocessing:
             logger.warning("You might want to use SPOS preprocessing if you are loading their checkpoints.")
@@ -52,7 +53,7 @@ if __name__ == "__main__":
     model.cuda()
     if torch.cuda.device_count() > 1:  # exclude last gpu, saving for data preprocessing on gpu
         model = nn.DataParallel(model, device_ids=list(range(0, torch.cuda.device_count() - 1)))
-    mutator = SPOSSupernetTrainingMutator(model, flops_func=model.module.get_candidate_flops,
+    mutator = SPOSSupernetTrainingMutator(model, flops_func=flops_func,
                                           flops_lb=290E6, flops_ub=360E6)
     criterion = CrossEntropyLabelSmooth(1000, args.label_smoothing)
     optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate,

examples/trials/mnist-pytorch/config_aml.yml

@@ -17,9 +17,9 @@ tuner:
 trial:
   command: python3 mnist.py
   codeDir: .
-  computeTarget: ${replace_to_your_computeTarget}
   image: msranni/nni
 amlConfig:
   subscriptionId: ${replace_to_your_subscriptionId}
   resourceGroup: ${replace_to_your_resourceGroup}
   workspaceName: ${replace_to_your_workspaceName}
+  computeTarget: ${replace_to_your_computeTarget}

examples/trials/mnist-tfv1/config_aml.yml

@@ -17,9 +17,9 @@ tuner:
 trial:
   command: python3 mnist.py
   codeDir: .
-  computeTarget: ${replace_to_your_computeTarget}
   image: msranni/nni
 amlConfig:
   subscriptionId: ${replace_to_your_subscriptionId}
   resourceGroup: ${replace_to_your_resourceGroup}
   workspaceName: ${replace_to_your_workspaceName}
+  computeTarget: ${replace_to_your_computeTarget}

nni/algorithms/nas/tensorflow/enas/trainer.py

@@ -13,21 +13,29 @@ from .mutator import EnasMutator
 logger = logging.getLogger(__name__)
 
 
-log_frequency = 100
-entropy_weight = 0.0001
-skip_weight = 0.8
-baseline_decay = 0.999
-child_steps = 500
-mutator_lr = 0.00035
-mutator_steps = 50
-mutator_steps_aggregate = 20
-aux_weight = 0.4
-test_arc_per_epoch = 1
-
-
 class EnasTrainer:
-    def __init__(self, model, loss, metrics, reward_function, optimizer, batch_size, num_epochs,
-                 dataset_train, dataset_valid):
+    def __init__(
+        self,
+        model,
+        loss,
+        metrics,
+        reward_function,
+        optimizer,
+        batch_size,
+        num_epochs,
+        dataset_train,
+        dataset_valid,
+        log_frequency=100,
+        entropy_weight=0.0001,
+        skip_weight=0.8,
+        baseline_decay=0.999,
+        child_steps=500,
+        mutator_lr=0.00035,
+        mutator_steps=50,
+        mutator_steps_aggregate=20,
+        aux_weight=0.4,
+        test_arc_per_epoch=1,
+    ):
         self.model = model
         self.loss = loss
         self.metrics = metrics
@@ -42,11 +50,21 @@ class EnasTrainer:
         self.valid_set = tf.data.Dataset.from_tensor_slices((x[split:], y[split:]))
         self.test_set = tf.data.Dataset.from_tensor_slices(dataset_valid)
 
-        self.mutator = EnasMutator(model)
-        self.mutator_optim = Adam(learning_rate=mutator_lr)
+        self.log_frequency = log_frequency
+        self.entropy_weight = entropy_weight
+        self.skip_weight = skip_weight
+        self.baseline_decay = baseline_decay
+        self.child_steps = child_steps
+        self.mutator_lr = mutator_lr
+        self.mutator_steps = mutator_steps
+        self.mutator_steps_aggregate = mutator_steps_aggregate
+        self.aux_weight = aux_weight
+        self.test_arc_per_epoch = test_arc_per_epoch
 
-        self.baseline = 0.
+        self.mutator = EnasMutator(model)
+        self.mutator_optim = Adam(learning_rate=self.mutator_lr)
 
+        self.baseline = 0.0
 
     def train(self, validate=True):
         for epoch in range(self.num_epochs):
@@ -58,14 +76,13 @@ class EnasTrainer:
     def validate(self):
         self.validate_one_epoch(-1)
 
-
     def train_one_epoch(self, epoch):
         train_loader, valid_loader = self._create_train_loader()
 
         # Sample model and train
         meters = AverageMeterGroup()
 
-        for step in range(1, child_steps + 1):
+        for step in range(1, self.child_steps + 1):
             x, y = next(train_loader)
             self.mutator.reset()
 
@@ -75,64 +92,88 @@ class EnasTrainer:
                     logits, aux_logits = logits
                     aux_loss = self.loss(aux_logits, y)
                 else:
-                    aux_loss = 0.
+                    aux_loss = 0.0
                 metrics = self.metrics(y, logits)
-                loss = self.loss(y, logits) + aux_weight * aux_loss
+                loss = self.loss(y, logits) + self.aux_weight * aux_loss
 
             grads = tape.gradient(loss, self.model.trainable_weights)
             grads = fill_zero_grads(grads, self.model.trainable_weights)
             grads, _ = tf.clip_by_global_norm(grads, 5.0)
             self.optimizer.apply_gradients(zip(grads, self.model.trainable_weights))
 
-            metrics['loss'] = tf.reduce_mean(loss).numpy()
+            metrics["loss"] = tf.reduce_mean(loss).numpy()
             meters.update(metrics)
 
-            if log_frequency and step % log_frequency == 0:
-                logger.info("Model Epoch [%d/%d] Step [%d/%d]  %s", epoch + 1,
-                            self.num_epochs, step, child_steps, meters)
+            if self.log_frequency and step % self.log_frequency == 0:
+                logger.info(
+                    "Model Epoch [%d/%d] Step [%d/%d]  %s",
+                    epoch + 1,
+                    self.num_epochs,
+                    step,
+                    self.child_steps,
+                    meters,
+                )
 
         # Train sampler (mutator)
         meters = AverageMeterGroup()
-        for mutator_step in range(1, mutator_steps + 1):
+        for mutator_step in range(1, self.mutator_steps + 1):
             grads_list = []
-            for step in range(1, mutator_steps_aggregate + 1):
+            for step in range(1, self.mutator_steps_aggregate + 1):
                 with tf.GradientTape() as tape:
                     x, y = next(valid_loader)
                     self.mutator.reset()
 
                     logits = self.model(x, training=False)
                     metrics = self.metrics(y, logits)
-                    reward = self.reward_function(y, logits) + entropy_weight * self.mutator.sample_entropy
-                    self.baseline = self.baseline * baseline_decay + reward * (1 - baseline_decay)
+                    reward = (
+                        self.reward_function(y, logits)
+                        + self.entropy_weight * self.mutator.sample_entropy
+                    )
+                    self.baseline = self.baseline * self.baseline_decay + reward * (
+                        1 - self.baseline_decay
+                    )
                     loss = self.mutator.sample_log_prob * (reward - self.baseline)
-                    loss += skip_weight * self.mutator.sample_skip_penalty
-
-                    meters.update({
-                        'reward': reward,
-                        'loss': tf.reduce_mean(loss).numpy(),
-                        'ent': self.mutator.sample_entropy.numpy(),
-                        'log_prob': self.mutator.sample_log_prob.numpy(),
-                        'baseline': self.baseline,
-                        'skip': self.mutator.sample_skip_penalty,
-                    })
-
-                    cur_step = step + (mutator_step - 1) * mutator_steps_aggregate
-                    if log_frequency and cur_step % log_frequency == 0:
-                        logger.info("RL Epoch [%d/%d] Step [%d/%d] [%d/%d]  %s", epoch + 1, self.num_epochs,
-                                    mutator_step, mutator_steps, step, mutator_steps_aggregate,
-                                    meters)
+                    loss += self.skip_weight * self.mutator.sample_skip_penalty
+
+                    meters.update(
+                        {
+                            "reward": reward,
+                            "loss": tf.reduce_mean(loss).numpy(),
+                            "ent": self.mutator.sample_entropy.numpy(),
+                            "log_prob": self.mutator.sample_log_prob.numpy(),
+                            "baseline": self.baseline,
+                            "skip": self.mutator.sample_skip_penalty,
+                        }
+                    )
+
+                    cur_step = step + (mutator_step - 1) * self.mutator_steps_aggregate
+                    if self.log_frequency and cur_step % self.log_frequency == 0:
+                        logger.info(
+                            "RL Epoch [%d/%d] Step [%d/%d] [%d/%d]  %s",
+                            epoch + 1,
+                            self.num_epochs,
+                            mutator_step,
+                            self.mutator_steps,
+                            step,
+                            self.mutator_steps_aggregate,
+                            meters,
+                        )
 
                 grads = tape.gradient(loss, self.mutator.trainable_weights)
                 grads = fill_zero_grads(grads, self.mutator.trainable_weights)
                 grads_list.append(grads)
-            total_grads = [tf.math.add_n(weight_grads) for weight_grads in zip(*grads_list)]
+            total_grads = [
+                tf.math.add_n(weight_grads) for weight_grads in zip(*grads_list)
+            ]
             total_grads, _ = tf.clip_by_global_norm(total_grads, 5.0)
-            self.mutator_optim.apply_gradients(zip(total_grads, self.mutator.trainable_weights))
+            self.mutator_optim.apply_gradients(
+                zip(total_grads, self.mutator.trainable_weights)
+            )
 
     def validate_one_epoch(self, epoch):
         test_loader = self._create_validate_loader()
 
-        for arc_id in range(test_arc_per_epoch):
+        for arc_id in range(self.test_arc_per_epoch):
             meters = AverageMeterGroup()
             for x, y in test_loader:
                 self.mutator.reset()
@@ -141,13 +182,17 @@ class EnasTrainer:
                     logits, _ = logits
                 metrics = self.metrics(y, logits)
                 loss = self.loss(y, logits)
-                metrics['loss'] = tf.reduce_mean(loss).numpy()
+                metrics["loss"] = tf.reduce_mean(loss).numpy()
                 meters.update(metrics)
 
-            logger.info("Test Epoch [%d/%d] Arc [%d/%d] Summary  %s",
-                        epoch + 1, self.num_epochs, arc_id + 1, test_arc_per_epoch,
-                        meters.summary())
-
+            logger.info(
+                "Test Epoch [%d/%d] Arc [%d/%d] Summary  %s",
+                epoch + 1,
+                self.num_epochs,
+                arc_id + 1,
+                self.test_arc_per_epoch,
+                meters.summary(),
+            )
 
     def _create_train_loader(self):
         train_set = self.train_set.shuffle(1000000).repeat().batch(self.batch_size)

nni/nas/benchmarks/nasbench201/model.py

@@ -31,7 +31,7 @@ class Nb201TrialConfig(Model):
         Dataset used for training and evaluation. NAS-Bench-201 provides the following 4 options:
         ``cifar10-valid`` (training data is splited into 25k for training and 25k for validation,
         validation data is used for test), ``cifar10`` (training data is used in training, validation
-        data is splited into 25k for validation and 25k for testing), ``cifar100`` (same protocol as ``cifar10``),
+        data is splited into 5k for validation and 5k for testing), ``cifar100`` (same protocol as ``cifar10``),
         and ``imagenet16-120`` (a subset of 120 classes in ImageNet, downscaled to 16x16, using training data
         for training, 6k images from validation set for validation and the other 6k for testing).
     """

nni/nas/tensorflow/mutator.py

@@ -66,7 +66,12 @@ class Mutator(BaseMutator):
         if reduction_type == 'mean':
             return sum(tensor_list) / len(tensor_list)
         if reduction_type == 'concat':
-            return tf.concat(tensor_list, axis=0)
+            image_data_format = tf.keras.backend.image_data_format()
+            if image_data_format == "channels_first":
+                axis = 0
+            else:
+                axis = -1
+            return tf.concat(tensor_list, axis=axis)
         raise ValueError('Unrecognized reduction policy: "{}'.format(reduction_type))
 
     def _get_decision(self, mutable):

nni/runtime/common.py

@@ -18,6 +18,11 @@ log_level_map = {
 
 _time_format = '%m/%d/%Y, %I:%M:%S %p'
 
+# FIXME
+# This hotfix the bug that querying installed tuners with `package_utils` will activate dispatcher logger.
+# This behavior depends on underlying implementation of `nnictl` and is likely to break in future.
+_logger_initialized = False
+
 class _LoggerFileWrapper(TextIOBase):
     def __init__(self, logger_file):
         self.file = logger_file
@@ -34,6 +39,11 @@ def init_logger(logger_file_path, log_level_name='info'):
     This will redirect anything from logging.getLogger() as well as stdout to specified file.
     logger_file_path: path of logger file (path-like object).
     """
+    global _logger_initialized
+    if _logger_initialized:
+        return
+    _logger_initialized = True
+
     if os.environ.get('NNI_PLATFORM') == 'unittest':
         return  # fixme: launching logic needs refactor
 
@@ -59,6 +69,11 @@ def init_standalone_logger():
     Initialize root logger for standalone mode.
     This will set NNI's log level to INFO and print its log to stdout.
     """
+    global _logger_initialized
+    if _logger_initialized:
+        return
+    _logger_initialized = True
+
     fmt = '[%(asctime)s] %(levelname)s (%(name)s) %(message)s'
     formatter = logging.Formatter(fmt, _time_format)
     handler = logging.StreamHandler(sys.stdout)

nni/tools/nnictl/launcher.py

@@ -373,6 +373,11 @@ def set_experiment(experiment_config, mode, port, config_file_name):
             {'key': 'frameworkcontroller_config', 'value': experiment_config['frameworkcontrollerConfig']})
         request_data['clusterMetaData'].append(
             {'key': 'trial_config', 'value': experiment_config['trial']})
+    elif experiment_config['trainingServicePlatform'] == 'aml':
+        request_data['clusterMetaData'].append(
+            {'key': 'aml_config', 'value': experiment_config['amlConfig']})
+        request_data['clusterMetaData'].append(
+            {'key': 'trial_config', 'value': experiment_config['trial']})
     response = rest_post(experiment_url(port), json.dumps(request_data), REST_TIME_OUT, show_error=True)
     if check_response(response):
         return response

nni/tools/trial_tool/trial.py

@@ -137,10 +137,15 @@ class Trial:
     def kill(self, trial_id=None):
         if trial_id == self.id or trial_id is None:
             if self.process is not None:
+                try:
                     nni_log(LogType.Info, "%s: killing trial" % self.name)
                     for child in psutil.Process(self.process.pid).children(True):
                         child.kill()
                     self.process.kill()
+                except psutil.NoSuchProcess:
+                    nni_log(LogType.Info, "kill trial %s failed: %s does not exist!" % (trial_id, self.process.pid))
+                except Exception as ex:
+                    nni_log(LogType.Error, "kill trial %s failed: %s " % (trial_id, str(ex)))
             self.cleanup()
 
     def cleanup(self):

test/config/training_service.yml

@@ -95,6 +95,8 @@ pai:
     containerNFSMountPath: 
     paiStorageConfigName: 
 remote:
+  remoteConfig:
+    reuse: false
   machineList:
   - ip:
     passwd: