AutoML for model compression (microsoft#2573)

azure-pipelines.yml

@@ -28,6 +28,7 @@ jobs:
       set -e
       sudo apt-get install -y pandoc
       python3 -m pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
+      python3 -m pip install tensorboardX==1.9
       python3 -m pip install tensorflow==2.2.0 --user
       python3 -m pip install keras==2.4.2 --user
       python3 -m pip install gym onnx peewee thop --user
@@ -68,6 +69,7 @@ jobs:
   - script: |
       set -e
       python3 -m pip install torch==1.3.1+cpu torchvision==0.4.2+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
+      python3 -m pip install tensorboardX==1.9
       python3 -m pip install tensorflow==1.15.2 --user
       python3 -m pip install keras==2.1.6 --user
       python3 -m pip install gym onnx peewee --user
@@ -117,6 +119,7 @@ jobs:
       set -e
       # pytorch Mac binary does not support CUDA, default is cpu version
       python3 -m pip install torchvision==0.6.0 torch==1.5.0 --user
+      python3 -m pip install tensorboardX==1.9
       python3 -m pip install tensorflow==1.15.2 --user
       brew install swig@3
       rm -f /usr/local/bin/swig
@@ -144,6 +147,7 @@ jobs:
       python -m pip install scikit-learn==0.23.2 --user
       python -m pip install keras==2.1.6 --user
       python -m pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
+      python -m pip install tensorboardX==1.9
       python -m pip install tensorflow==1.15.2 --user
     displayName: 'Install dependencies'
   - script: |

docs/en_US/Compressor/Pruner.md

@@ -20,6 +20,7 @@ We provide several pruning algorithms that support fine-grained weight pruning a
 * [NetAdapt Pruner](#netadapt-pruner)
 * [SimulatedAnnealing Pruner](#simulatedannealing-pruner)
 * [AutoCompress Pruner](#autocompress-pruner)
+* [AutoML for Model Compression Pruner](#automl-for-model-compression-pruner)
 * [Sensitivity Pruner](#sensitivity-pruner)
 
 **Others**
@@ -476,6 +477,39 @@ You can view [example](https://github.com/microsoft/nni/blob/master/examples/mod
 ..  autoclass:: nni.compression.torch.AutoCompressPruner
 ```
 
+## AutoML for Model Compression Pruner
+
+AutoML for Model Compression Pruner (AMCPruner) leverages reinforcement learning to provide the model compression policy.
+This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio,
+better preserving the accuracy and freeing human labor.
+
+![](../../img/amc_pruner.jpg)
+
+For more details, please refer to [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://arxiv.org/pdf/1802.03494.pdf).
+
+
+#### Usage
+
+PyTorch code
+
+```python
+from nni.compression.torch import AMCPruner
+config_list = [{
+        'op_types': ['Conv2d', 'Linear']
+    }]
+pruner = AMCPruner(model, config_list, evaluator, val_loader, flops_ratio=0.5)
+pruner.compress()
+```
+
+You can view [example](https://github.com/microsoft/nni/blob/master/examples/model_compress/amc/) for more information.
+
+#### User configuration for AutoCompress Pruner
+
+##### PyTorch
+
+```eval_rst
+..  autoclass:: nni.compression.torch.AMCPruner
+```
 
 ## ADMM Pruner
 Alternating Direction Method of Multipliers (ADMM) is a mathematical optimization technique,

examples/model_compress/amc/amc_search.py

@@ -0,0 +1,136 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import sys
+import argparse
+import time
+
+import torch
+import torch.nn as nn
+
+from nni.compression.torch import AMCPruner
+from data import get_split_dataset
+from utils import AverageMeter, accuracy
+
+sys.path.append('../models')
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='AMC search script')
+    parser.add_argument('--model_type', default='mobilenet', type=str, choices=['mobilenet', 'mobilenetv2'], 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('--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')
+    parser.add_argument('--ckpt_path', default=None, type=str, help='manual path of checkpoint')
+
+    parser.add_argument('--train_episode', default=800, type=int, help='number of training episode')
+    parser.add_argument('--n_gpu', default=1, type=int, help='number of gpu to use')
+    parser.add_argument('--n_worker', default=16, type=int, help='number of data loader worker')
+    parser.add_argument('--job', default='train_export', type=str, choices=['train_export', 'export_only'],
+                        help='search best pruning policy and export or just export model with searched policy')
+    parser.add_argument('--export_path', default=None, type=str, help='path for exporting models')
+    parser.add_argument('--searched_model_path', default=None, type=str, help='path for searched best wrapped model')
+
+    return parser.parse_args()
+
+
+def get_model_and_checkpoint(model, dataset, checkpoint_path, n_gpu=1):
+    if model == 'mobilenet' and dataset == 'imagenet':
+        from mobilenet import MobileNet
+        net = MobileNet(n_class=1000)
+    elif model == 'mobilenetv2' and dataset == 'imagenet':
+        from mobilenet_v2 import MobileNetV2
+        net = MobileNetV2(n_class=1000)
+    elif model == 'mobilenet' and dataset == 'cifar10':
+        from mobilenet import MobileNet
+        net = MobileNet(n_class=10)
+    elif model == 'mobilenetv2' and dataset == 'cifar10':
+        from mobilenet_v2 import MobileNetV2
+        net = MobileNetV2(n_class=10)
+    else:
+        raise NotImplementedError
+    if checkpoint_path:
+        print('loading {}...'.format(checkpoint_path))
+        sd = torch.load(checkpoint_path, map_location=torch.device('cpu'))
+        if 'state_dict' in sd:  # a checkpoint but not a state_dict
+            sd = sd['state_dict']
+        sd = {k.replace('module.', ''): v for k, v in sd.items()}
+        net.load_state_dict(sd)
+
+    if torch.cuda.is_available() and n_gpu > 0:
+        net = net.cuda()
+        if n_gpu > 1:
+            net = torch.nn.DataParallel(net, range(n_gpu))
+
+    return net
+
+def init_data(args):
+    # split the train set into train + val
+    # for CIFAR, split 5k for val
+    # for ImageNet, split 3k for val
+    val_size = 5000 if 'cifar' in args.dataset else 3000
+    train_loader, val_loader, _ = get_split_dataset(
+        args.dataset, args.batch_size,
+        args.n_worker, val_size,
+        data_root=args.data_root,
+        shuffle=False
+    )  # same sampling
+    return train_loader, val_loader
+
+def validate(val_loader, model, verbose=False):
+    batch_time = AverageMeter()
+    losses = AverageMeter()
+    top1 = AverageMeter()
+    top5 = AverageMeter()
+
+    criterion = nn.CrossEntropyLoss().cuda()
+    # switch to evaluate mode
+    model.eval()
+    end = time.time()
+
+    t1 = time.time()
+    with torch.no_grad():
+        for i, (input, target) in enumerate(val_loader):
+            target = target.to(device)
+            input_var = torch.autograd.Variable(input).to(device)
+            target_var = torch.autograd.Variable(target).to(device)
+
+            # compute output
+            output = model(input_var)
+            loss = criterion(output, target_var)
+
+            # measure accuracy and record loss
+            prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
+            losses.update(loss.item(), input.size(0))
+            top1.update(prec1.item(), input.size(0))
+            top5.update(prec5.item(), input.size(0))
+
+            # measure elapsed time
+            batch_time.update(time.time() - end)
+            end = time.time()
+    t2 = time.time()
+    if verbose:
+        print('* Test loss: %.3f    top1: %.3f    top5: %.3f    time: %.3f' %
+              (losses.avg, top1.avg, top5.avg, t2 - t1))
+    return top5.avg
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    device = torch.device('cuda') if torch.cuda.is_available() and args.n_gpu > 0 else torch.device('cpu')
+
+    model = get_model_and_checkpoint(args.model_type, args.dataset, checkpoint_path=args.ckpt_path, n_gpu=args.n_gpu)
+    _, val_loader = init_data(args)
+
+    config_list = [{
+        'op_types': ['Conv2d', 'Linear']
+    }]
+    pruner = AMCPruner(
+        model, config_list, validate, val_loader, model_type=args.model_type, dataset=args.dataset,
+        train_episode=args.train_episode, job=args.job, export_path=args.export_path,
+        searched_model_path=args.searched_model_path,
+        flops_ratio=args.flops_ratio, lbound=args.lbound, rbound=args.rbound)
+    pruner.compress()