【Fleet2.0 Util】 add documents

python/paddle/distributed/fleet/base/role_maker.py

@@ -637,7 +637,7 @@ def __get_default_iface_from_interfaces(self):
         return "lo"
 
     def __start_kv_server(self, http_server_d, size_d):
-        from paddle.distributed.fleet.utils import KVServer
+        from paddle.distributed.fleet.utils.http_server import KVServer
         http_server = KVServer(int(self._http_ip_port[1]), size_d)
         http_server.start()
         wait_seconds = 5
@@ -651,6 +651,7 @@ class UserDefinedRoleMaker(PaddleCloudRoleMaker):
     def __init__(self, is_collective=False, init_gloo=False, **kwargs):
         super(UserDefinedRoleMaker, self).__init__(
             is_collective=is_collective, init_gloo=init_gloo, **kwargs)
+        self._init_gloo = init_gloo
 
     def _user_defined_ps_env(self):
         self._server_endpoints = self._kwargs.get("server_endpoints")

python/paddle/distributed/fleet/base/util_factory.py

@@ -16,20 +16,18 @@
 """basic collective operations in python"""
 """remote file system"""
 
-__all__ = ['UtilBase']
-
-import numpy as np
-import os
-
-import subprocess
-from paddle.fluid import core
-from collections import OrderedDict
-import paddle.fluid as fluid
-from google.protobuf import text_format
-from paddle.fluid import debugger
-from paddle.fluid.framework import Program
-from paddle.fluid.proto import framework_pb2
 from ..utils.fs import FS, LocalFS, HDFSClient
+from paddle.fluid.proto import framework_pb2
+from paddle.fluid.framework import Program
+from paddle.fluid import debugger
+from google.protobuf import text_format
+import paddle.fluid as fluid
+from collections import OrderedDict
+from paddle.fluid import core
+import subprocess
+import os
+import numpy as np
+__all__ = ['UtilBase']
 
 
 class UtilFactory(object):
@@ -53,7 +51,7 @@ def _set_strategy(self, dist_strategy):
     def _set_role_maker(self, role_maker):
         self.role_maker = role_maker
 
-    def set_file_system(self, fs_client):
+    def _set_file_system(self, fs_client):
         assert isinstance(
             fs_client, FS
         ), "fs_client must be the instance of paddle.distributed.fleet.utils.FS"
@@ -87,36 +85,183 @@ def __check_comm_world(self, comm_world="worker"):
         return _comm_world
 
     def all_reduce(self, input, mode, comm_world="worker"):
+        """
+        All reduce `input` between specified collection. This is a distributed API.
+
+        Args:
+            input (list|numpy.array): The input variable to do all_reduce between specified collection.
+            mode (str): "sum" or "min" or "max".
+            comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
+
+        Returns:
+            output(Numpy.array|None): A numpy array with the same shape as the `input` .
+
+        Examples:
+            .. code-block:: python
+
+                # Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
+                from paddle.distributed.fleet.base.util_factory import fleet_util
+                import paddle.distributed.fleet as fleet
+                from paddle.distributed.fleet import PaddleCloudRoleMaker
+                import sys
+                import numpy as np
+
+                def train():
+                    role = PaddleCloudRoleMaker(
+                        is_collective=False,
+                        init_gloo=True,
+                        path="./tmp_gloo")
+                    fleet.init(role)
+                    fleet_util._set_role_maker(role)
+
+                    if fleet.is_server():
+                        input = [1, 2]
+                        output = fleet_util.all_reduce(input, "sum", "server")
+                        print(output)
+                        # [2, 4]
+                    elif fleet.is_worker():
+                        input = np.array([3, 4])
+                        output = fleet_util.all_reduce(input, "sum", "worker")
+                        print(output)
+                        # [6, 8]
+                    output = fleet_util.all_reduce(input, "sum", "all")
+                    print(output)
+                    # [8, 12]
+                if __name__ == "__main__":
+                    train()
+        """
         _comm_world = self.__check_comm_world(comm_world)
         return self.role_maker._all_reduce(_comm_world, input, mode)
 
     def barrier(self, comm_world="worker"):
+        """
+        Barrier between specified collection.
+
+        Args:
+            comm_world (str, optional): Collection used to execute barrier operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
+
+        Examples:
+
+            .. code-block:: python
+                # Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
+
+                from paddle.distributed.fleet.base.util_factory import fleet_util
+                import paddle.distributed.fleet as fleet
+                from paddle.distributed.fleet import PaddleCloudRoleMaker
+                import sys
+
+                def train():
+                    role = PaddleCloudRoleMaker(
+                        is_collective=False,
+                        init_gloo=True,
+                        path="./tmp_gloo")
+                    fleet.init(role)
+                    fleet_util._set_role_maker(role)
+
+                    if fleet.is_server():
+                        fleet_util.barrier("server")
+                        print("all server arrive here")
+                    elif fleet.is_worker():
+                        fleet_util.barrier("worker")
+                        print("all server arrive here")
+                    fleet_util.barrier("all")
+                    print("all servers and workers arrive here")
+
+                if __name__ == "__main__":
+                    train()
+        """
         _comm_world = self.__check_comm_world(comm_world)
         self.role_maker._barrier(_comm_world)
 
     def all_gather(self, input, comm_world="worker"):
+        """
+        All gather `input` between specified collection.
+
+        Args:
+            input (Int|Float): The input variable to do all_gather between specified collection.
+            comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
+
+        Returns:
+            output (List): A list of gathered values.
+
+        Examples:
+
+            .. code-block:: python
+
+                # Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
+                from paddle.distributed.fleet.base.util_factory import fleet_util
+                import paddle.distributed.fleet as fleet
+                from paddle.distributed.fleet import PaddleCloudRoleMaker
+                import sys
+
+                def train():
+                    role = PaddleCloudRoleMaker(
+                        is_collective=False,
+                        init_gloo=True,
+                        path="./tmp_gloo")
+                    fleet.init(role)
+                    fleet_util._set_role_maker(role)
+
+                    if fleet.is_server():
+                        input = fleet.server_index()
+                        output = fleet_util.all_gather(input, "server")
+                        print(output)
+                        # output = [0, 1]
+                    elif fleet.is_worker():
+                        input = fleet.worker_index()
+                        output = fleet_util.all_gather(input, "worker")
+                        # output = [0, 1]
+                        print(output)
+                    output = fleet_util.all_gather(input, "all")
+                    print(output)
+                    # output = [0, 1, 0, 1]
+
+                if __name__ == "__main__":
+                    train()
+        """
         _comm_world = self.__check_comm_world(comm_world)
         return self.role_maker._all_gather(_comm_world, input)
 
-    def broadcast(self):
+    def _broadcast(self):
         pass
 
-    def scatter(self):
+    def _scatter(self):
         pass
 
     def get_file_shard(self, files):
         """
-        split files before distributed training,
-        example 1: files is [a, b, c ,d, e]  and trainer_num = 2, then trainer
-                   0 gets [a, b, c] and trainer 1 gets [d, e].
-        example 2: files is [a, b], and trainer_num = 3, then trainer 0 gets
-                   [a], trainer 1 gets [b],  trainer 2 gets []
+        Split files before distributed training, and return filelist assigned to the current trainer.
+
+        .. code-block:: text
+
+            example 1: files is [a, b, c ,d, e]  and trainer_num = 2, then trainer
+                    0 gets [a, b, c] and trainer 1 gets [d, e].
+            example 2: files is [a, b], and trainer_num = 3, then trainer 0 gets
+                    [a], trainer 1 gets [b],  trainer 2 gets []
 
         Args:
-            files(list): file list need to be read.
+            files(list): File list need to be read.
 
         Returns:
-            list: files belongs to this worker.
+            List: Files belong to this worker.
+
+        Examples:
+
+            .. code-block:: python
+
+                from paddle.distributed.fleet.base.util_factory import fleet_util
+                import paddle.distributed.fleet.base.role_maker as role_maker
+
+                role = role_maker.UserDefinedRoleMaker(
+                    is_collective=False,
+                    init_gloo=False,
+                    current_id=0,
+                    role=role_maker.Role.WORKER,
+                    worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
+                    server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
+                fleet_util._set_role_maker(role)
+                files = fleet_util.get_file_shard(["file1", "file2", "file3"])
+                # files = ["file1", "file2"]
         """
         if not isinstance(files, list):
             raise TypeError("files should be a list of file need to be read.")
@@ -140,6 +285,30 @@ def get_file_shard(self, files):
         return trainer_files[trainer_id]
 
     def print_on_rank(self, message, rank_id):
+        """
+        Woker of rank `rank_id` print some message. 
+
+        Args:
+            message(str): Log to be printed.
+            rank_id(int): trainer id.
+
+        Examples:
+
+            .. code-block:: python
+
+                from paddle.distributed.fleet.base.util_factory import fleet_util
+                import paddle.distributed.fleet.base.role_maker as role_maker
+
+                role = role_maker.UserDefinedRoleMaker(
+                    is_collective=False,
+                    init_gloo=False,
+                    current_id=0,
+                    role=role_maker.Role.WORKER,
+                    worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
+                    server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
+                fleet_util._set_role_maker(role)
+                fleet_util.print_on_rank("I'm worker 0", 0)
+        """
         if self.role_maker.worker_index() != rank_id:
             return
         print(message)
@@ -297,7 +466,7 @@ def check_not_expected_ops(prog, not_expected_op_types):
         with fluid.scope_guard(scope):
             inference_program, feed_target_names, fetch_targets = \
                 fluid.io.load_inference_model(config.dump_model_dir, exe, model_filename=model_filename,
-                                            params_filename=config.save_params_filename)
+                                              params_filename=config.save_params_filename)
 
             # check program vars and saved vars shape
             orig_para_shape = {

python/paddle/distributed/fleet/launch.py

@@ -87,7 +87,7 @@ def _parse_args():
 see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
 ''')
 
-    #Optional arguments for the launch helper
+    # Optional arguments for the launch helper
     parser.add_argument(
         "--ips",
         type=str,
@@ -115,7 +115,7 @@ def _parse_args():
         default="log",
         help="The path for each process's log.If it's not set, the log will printed to default pipe."
     )
-    #positional
+    # positional
     parser.add_argument(
         "training_script",
         type=str,
@@ -124,7 +124,7 @@ def _parse_args():
         "followed by all the arguments for the "
         "training script")
 
-    #rest from the training program
+    # rest from the training program
     parser.add_argument('training_script_args', nargs=REMAINDER)
     return parser.parse_args()
 
@@ -138,7 +138,7 @@ def get_cluster_from_args(args, gpus):
 
     # node_ip = args.node_ip
     assert node_ip in node_ips, "Can't find your local ip {%s} in node_ips: {%s}" \
-                % (node_ip, node_ips)
+        % (node_ip, node_ips)
     node_rank = node_ips.index(node_ip)
 
     logger.debug("parsed from args: node_ips:{} node_ip:{} node_rank:{}".format(
@@ -280,7 +280,7 @@ def launch_ps(args):
         _, current_node_ip = get_host_name_ip()
 
     assert current_node_ip in node_ips, "Can't find your local ip {%s} in args.servers and args.workers ips: {%s}" \
-                % (current_node_ip, node_ips)
+        % (current_node_ip, node_ips)
     node_rank = node_ips.index(current_node_ip)
     logger.debug(
         "parsed from args: node_ips:{} current_node_ip:{} node_rank:{}, server_ports:{}".
@@ -323,10 +323,12 @@ def launch_ps(args):
     for idx, cur_server in enumerate(pod.servers):
         proc_env = {
             "PADDLE_PSERVERS_IP_PORT_LIST": server_endpoints,
+            "PADDLE_TRAINER_ENDPOINTS": worker_endpoints,
             "PADDLE_PORT": cur_server.endpoint.split(":")[1],
             "TRAINING_ROLE": "PSERVER",
             "PADDLE_TRAINERS_NUM": str(worker_num),
-            "POD_IP": cur_server.endpoint.split(":")[0]
+            "POD_IP": cur_server.endpoint.split(":")[0],
+            "PADDLE_WITH_GLOO": "1"
         }
         current_env.update(proc_env)
 
@@ -365,7 +367,8 @@ def launch_ps(args):
             "PADDLE_TRAINER_ENDPOINTS": worker_endpoints,
             "PADDLE_TRAINERS_NUM": str(worker_num),
             "TRAINING_ROLE": "TRAINER",
-            "PADDLE_TRAINER_ID": str(cur_worker.rank)
+            "PADDLE_TRAINER_ID": str(cur_worker.rank),
+            "PADDLE_WITH_GLOO": "1"
         }
         current_env.update(proc_env)
 
@@ -430,7 +433,11 @@ def launch():
         co_arg for co_arg in collective_args
         if co_arg in " ".join(sys.argv[1:-1])
     ]
-    cuda_device_num = fluid.core.get_cuda_device_count()
+    if fluid.core.is_compiled_with_cuda():
+        cuda_device_num = fluid.core.get_cuda_device_count()
+    else:
+        cuda_device_num = 0
+
     if len(has_ps_args) > 0 or cuda_device_num == 0:
         logger.info(
             "Run parameter-sever cpu mode. pserver arguments:{}, cuda count:{}".

python/paddle/distributed/fleet/utils/__init__.py

@@ -11,8 +11,3 @@
 # 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.
-
-from .fs import *
-from .http_server import KVHandler, KVHTTPServer, KVServer
-
-#__all__ = ['KVHandler', 'KVHTTPServer', 'KVServer'] + fs.__all__