replace with_data_parallel with fleet (#1626)

demo/distillation/README.md

@@ -13,7 +13,7 @@
 默认配置:
 
 ```yaml
-batch_size: 256
+batch_size: 64
 init_lr: 0.1
 lr_strategy: piecewise_decay
 l2_decay: 3e-5
@@ -21,14 +21,14 @@ momentum_rate: 0.9
 num_epochs: 120
 data: imagenet
 ```
-训练使用默认配置启动即可
+训练使用默认配置启动即可。这里的batch_size指每张卡上的batch_size。
 
 ### 2. 启动训练
 
 在配置好ImageNet数据集后，用以下命令启动训练即可:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python distill.py
+CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch distill.py
 ```
 
 ### 3. 训练结果

demo/distillation/distill.py

@@ -15,6 +15,9 @@ import models
 from utility import add_arguments, print_arguments, _download, _decompress
 from paddleslim.dist import merge, l2, soft_label
 
+from paddle.distributed import fleet
+from paddle.distributed.fleet import DistributedStrategy
+
 logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
 _logger = logging.getLogger(__name__)
 _logger.setLevel(logging.INFO)
@@ -76,6 +79,9 @@ def create_optimizer(args):
 
 
 def compress(args):
+
+    fleet.init(is_collective=True)
+
     if args.data == "cifar10":
         train_dataset = paddle.vision.datasets.Cifar10(mode='train')
         val_dataset = paddle.vision.datasets.Cifar10(mode='test')
@@ -103,38 +109,38 @@ def compress(args):
     else:
         devices_num = int(os.environ.get('CPU_NUM', 1))
     with paddle.static.program_guard(student_program, s_startup):
-        with paddle.utils.unique_name.guard():
-            image = paddle.static.data(
-                name='image', shape=[None] + image_shape, dtype='float32')
-            label = paddle.static.data(
-                name='label', shape=[None, 1], dtype='int64')
-            train_loader = paddle.io.DataLoader(
-                train_dataset,
-                places=places,
-                feed_list=[image, label],
-                drop_last=True,
-                batch_size=int(args.batch_size / devices_num),
-                return_list=False,
-                shuffle=True,
-                use_shared_memory=True,
-                num_workers=4)
-            valid_loader = paddle.io.DataLoader(
-                val_dataset,
-                places=place,
-                feed_list=[image, label],
-                drop_last=False,
-                return_list=False,
-                use_shared_memory=True,
-                batch_size=args.batch_size,
-                shuffle=False)
-            # model definition
-            model = models.__dict__[args.model]()
-            out = model.net(input=image, class_dim=class_dim)
-            cost = paddle.nn.functional.loss.cross_entropy(
-                input=out, label=label)
-            avg_cost = paddle.mean(x=cost)
-            acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
-            acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
+        image = paddle.static.data(
+            name='image', shape=[None] + image_shape, dtype='float32')
+        label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+        sampler = paddle.io.DistributedBatchSampler(
+            train_dataset,
+            shuffle=False,
+            drop_last=True,
+            batch_size=args.batch_size)
+        train_loader = paddle.io.DataLoader(
+            train_dataset,
+            places=places,
+            feed_list=[image, label],
+            batch_sampler=sampler,
+            return_list=False,
+            use_shared_memory=False,
+            num_workers=4)
+        valid_loader = paddle.io.DataLoader(
+            val_dataset,
+            places=place,
+            feed_list=[image, label],
+            drop_last=False,
+            return_list=False,
+            use_shared_memory=False,
+            batch_size=args.batch_size,
+            shuffle=False)
+        # model definition
+        model = models.__dict__[args.model]()
+        out = model.net(input=image, class_dim=class_dim)
+        cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
+        avg_cost = paddle.mean(x=cost)
+        acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
+        acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
 
     val_program = student_program.clone(for_test=True)
     exe = paddle.static.Executor(place)
@@ -172,18 +178,19 @@ def compress(args):
     data_name_map = {'image': 'image'}
     merge(teacher_program, student_program, data_name_map, place)
 
+    build_strategy = paddle.static.BuildStrategy()
+    dist_strategy = DistributedStrategy()
+    dist_strategy.build_strategy = build_strategy
+
     with paddle.static.program_guard(student_program, s_startup):
         distill_loss = soft_label("teacher_fc_0.tmp_0", "fc_0.tmp_0",
                                   student_program)
         loss = avg_cost + distill_loss
         lr, opt = create_optimizer(args)
+        opt = fleet.distributed_optimizer(opt, strategy=dist_strategy)
         opt.minimize(loss)
     exe.run(s_startup)
-    build_strategy = paddle.static.BuildStrategy()
-    build_strategy.fuse_all_reduce_ops = False
-    parallel_main = paddle.static.CompiledProgram(
-        student_program).with_data_parallel(
-            loss_name=loss.name, build_strategy=build_strategy)
+    parallel_main = student_program
 
     for epoch_id in range(args.num_epochs):
         for step_id, data in enumerate(train_loader):