collective

doc/collective_mode.md

@@ -0,0 +1,51 @@
+# Collective模式运行
+如果您希望可以同时使用多张GPU，更为快速的训练您的模型，可以尝试使用`单机多卡`或`多机多卡`模式运行。
+
+## 版本要求
+用户需要确保已经安装paddlepaddle-2.0.0-rc-gpu及以上版本的飞桨开源框架
+
+## 设置config.yaml
+首先需要在模型的yaml配置中，加入use_fleet参数，并把值设置成True。  
+```yaml
+runner:
+  # 通用配置不再赘述
+  ...
+  # use fleet
+  use_fleet: True
+```
+## 单机多卡训练
+
+### 单机多卡模式下指定需要使用的卡号
+在没有进行设置的情况下将使用单机上所有gpu卡。若需要指定部分gpu卡执行，可以通过设置环境变量CUDA_VISIBLE_DEVICES来实现。  
+例如单机上有8张卡，只打算用前4卡张训练，可以设置export CUDA_VISIBLE_DEVICES=0,1,2,3  
+再执行训练脚本即可。
+
+### 执行训练
+```bash
+# 动态图执行训练
+python -m paddle.distributed.launch ../../../tools/trainer.py -m config.yaml
+# 静态图执行训练
+python -m paddle.distributed.launch ../../../tools/static_trainer.py -m config.yaml
+```
+
+注意：在使用静态图训练时，确保模型static_model.py程序中create_optimizer函数设置了分布式优化器。
+```python
+def create_optimizer(self, strategy=None):
+    optimizer = paddle.optimizer.Adam(learning_rate=self.learning_rate, lazy_mode=True)
+    # 通过Fleet API获取分布式优化器，将参数传入飞桨的基础优化器
+    if strategy != None:
+        import paddle.distributed.fleet as fleet
+        optimizer = fleet.distributed_optimizer(optimizer, strategy)
+    optimizer.minimize(self._cost)
+```
+
+## 多机多卡训练
+使用多机多卡训练，您需要另外一台或多台能够互相ping通的机器。每台机器中都需要安装paddlepaddle-2.0.0-rc-gpu及以上版本的飞桨开源框架，同时将需要运行的paddlerec模型，数据集复制到每一台机器上。
+从单机多卡到多机多卡训练，在代码上不需要做任何改动，只需再额外指定ips参数即可。其内容为多机的ip列表，命令如下所示：
+```bash
+# 动态图
+# 动态图执行训练
+python -m paddle.distributed.launch --ips="xx.xx.xx.xx,yy.yy.yy.yy" --gpus 0,1,2,3,4,5,6,7 ../../../tools/trainer.py -m config.yaml
+# 静态图执行训练
+python -m paddle.distributed.launch --ips="xx.xx.xx.xx,yy.yy.yy.yy" --gpus 0,1,2,3,4,5,6,7 ../../../tools/static_trainer.py -m config.yaml
+```

models/rank/wide_deep/config.yaml

@@ -17,9 +17,9 @@
 runner:
   train_data_dir: "data/sample_data/train"
   train_reader_path: "criteo_reader" # importlib format
-  use_gpu: False
+  use_gpu: True
   use_auc: True
-  train_batch_size: 2
+  train_batch_size: 50
   epochs: 3
   print_interval: 2
   #model_init_path: "output_model/0" # init model
@@ -34,6 +34,8 @@ runner:
   use_inference: False
   save_inference_feed_varnames: ["label","C1","C2","C3","C4","C5","C6","C7","C8","C9","C10","C11","C12","C13","C14","C15","C16","C17","C18","C19","C20","C21","C22","C23","C24","C25","C26","dense_input"]
   save_inference_fetch_varnames: ["cast_0.tmp_0"]
+  #use fleet
+  use_fleet: False
 
 # hyper parameters of user-defined network
 hyper_parameters:

tools/static_trainer.py

@@ -63,9 +63,9 @@ def main(args):
     input_data_names = [data.name for data in input_data]
 
     fetch_vars = static_model_class.net(input_data)
+
     #infer_target_var = model.infer_target_var
     logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
-    static_model_class.create_optimizer()
 
     use_gpu = config.get("runner.use_gpu", True)
     use_auc = config.get("runner.use_auc", False)
@@ -79,6 +79,7 @@ def main(args):
     model_init_path = config.get("runner.model_init_path", None)
     batch_size = config.get("runner.train_batch_size", None)
     reader_type = config.get("runner.reader_type", "DataLoader")
+    use_fleet = config.get("runner.use_fleet", False)
     os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
     logger.info("**************common.configs**********")
     logger.info(
@@ -88,6 +89,16 @@ def main(args):
     logger.info("**************common.configs**********")
 
     place = paddle.set_device('gpu' if use_gpu else 'cpu')
+
+    if use_fleet:
+        from paddle.distributed import fleet
+        strategy = fleet.DistributedStrategy()
+        fleet.init(is_collective=True, strategy=strategy)
+    if use_fleet:
+        static_model_class.create_optimizer(strategy)
+    else:
+        static_model_class.create_optimizer()
+
     exe = paddle.static.Executor(place)
     # initialize
     exe.run(paddle.static.default_startup_program())

tools/trainer.py

@@ -79,6 +79,7 @@ def main(args):
     train_batch_size = config.get("runner.train_batch_size", None)
     model_save_path = config.get("runner.model_save_path", "model_output")
     model_init_path = config.get("runner.model_init_path", None)
+    use_fleet = config.get("runner.use_fleet", False)
 
     logger.info("**************common.configs**********")
     logger.info(
@@ -102,6 +103,14 @@ def main(args):
     # to do : add optimizer function
     optimizer = dy_model_class.create_optimizer(dy_model, config)
 
+    # use fleet run collective
+    if use_fleet:
+        from paddle.distributed import fleet
+        strategy = fleet.DistributedStrategy()
+        fleet.init(is_collective=True, strategy=strategy)
+        optimizer = fleet.distributed_optimizer(optimizer)
+        dy_model = fleet.distributed_model(dy_model)
+
     logger.info("read data")
     train_dataloader = create_data_loader(config=config, place=place)