[LAUNCH] add distributed launch check tools (#44495)

* add launch test

* launch test for cpu

* bs 1

python/paddle/distributed/launch/context/__init__.py

@@ -101,6 +101,7 @@ class Context(object):
             return False
 
     def set_env_in_args(self):
+        # this logic may not propre to replace args with env, but ...
         for k, v in env_args_mapping.items():
             if k in self.envs:
-                setattr(self.args, v, self.envs[k])
+                setattr(self.args, v, type(getattr(self.args, v))(self.envs[k]))

python/paddle/distributed/launch/controllers/collective.py

@@ -97,10 +97,14 @@ class CollectiveController(Controller):
                 "PADDLE_TRAINERS_NUM": "{}".format(global_size),
                 "PADDLE_RANK_IN_NODE": str(i),
             }
+            if len(selected_dev_list) > 0:
                 if self.pod.replicas == 1:
                     e.update({selected_dev_key: ",".join(selected_dev_list)})
                 else:
                     e.update({selected_dev_key: selected_dev_list[i]})
+            else:
+                e.update({'PADDLE_DISTRI_BACKEND': 'gloo'})
+
             self.add_container(envs=e, log_tag=i)
 
         return True

python/paddle/distributed/launch/plugins/__init__.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import six
+import os
 
 __all__ = []
 
@@ -60,4 +61,15 @@ def rewrite_host_ip(ctx):
         ctx.node.ip = ctx.args.host
 
 
-enabled_plugins = [collective_compatible, rewrite_host_ip, process_args]
+def test_mode(ctx):
+    if ctx.args.training_script == 'test':
+        ctx.logger.info('Paddle Distributed Test begin...')
+        if int(ctx.args.nnodes) < 2:
+            ctx.args.nnodes = 2
+        ctx.args.training_script = '{}/test.py'.format(
+            os.path.dirname(__file__))
+
+
+enabled_plugins = [
+    test_mode, collective_compatible, rewrite_host_ip, process_args
+]

python/paddle/distributed/launch/plugins/test.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import paddle
+from paddle.distributed import fleet
+from paddle.vision.models import ResNet
+from paddle.vision.models.resnet import BottleneckBlock
+from paddle.io import Dataset, BatchSampler, DataLoader
+
+base_lr = 0.1
+momentum_rate = 0.9
+l2_decay = 1e-4
+
+epoch = 3
+batch_num = 1
+batch_size = 1
+class_dim = 102
+
+
+# define a random dataset
+class RandomDataset(Dataset):
+
+    def __init__(self, num_samples):
+        self.num_samples = num_samples
+
+    def __getitem__(self, idx):
+        image = np.random.random([3, 224, 224]).astype('float32')
+        label = np.random.randint(0, class_dim - 1, (1, )).astype('int64')
+        return image, label
+
+    def __len__(self):
+        return self.num_samples
+
+
+def optimizer_setting(parameter_list=None):
+    optimizer = paddle.optimizer.Momentum(
+        learning_rate=base_lr,
+        momentum=momentum_rate,
+        weight_decay=paddle.regularizer.L2Decay(l2_decay),
+        parameters=parameter_list)
+    return optimizer
+
+
+def train_resnet():
+    fleet.init(is_collective=True)
+
+    resnet = ResNet(BottleneckBlock, 18, num_classes=class_dim)
+    optimizer = optimizer_setting(parameter_list=resnet.parameters())
+    optimizer = fleet.distributed_optimizer(optimizer)
+    resnet = fleet.distributed_model(resnet)
+
+    dataset = RandomDataset(batch_num * batch_size)
+    train_loader = DataLoader(dataset,
+                              batch_size=batch_size,
+                              shuffle=True,
+                              drop_last=True,
+                              num_workers=2)
+
+    print("Distributed training start...")
+    for eop in range(epoch):
+        resnet.train()
+
+        for batch_id, data in enumerate(train_loader()):
+            img, label = data
+            label.stop_gradient = True
+
+            out = resnet(img)
+            loss = paddle.nn.functional.cross_entropy(input=out, label=label)
+            avg_loss = paddle.mean(x=loss)
+            acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
+            acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
+
+            avg_loss.backward()
+            optimizer.step()
+            resnet.clear_gradients()
+
+            print("[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f" %
+                  (eop, batch_id, avg_loss, acc_top1, acc_top5))
+
+    print("Distributed training completed")
+
+
+if __name__ == '__main__':
+    import os
+    nnodes = os.getenv('PADDLE_NNODES')
+    cn = os.getenv('PADDLE_LOCAL_SIZE')
+    print(f"Prepare distributed training with {nnodes} nodes {cn} cards")
+    train_resnet()