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586 lines
24 KiB
586 lines
24 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Fleet Utils."""
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"""distributed operations"""
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"""basic collective operations in python"""
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"""remote file system"""
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from ..utils.fs import FS, LocalFS, HDFSClient
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from paddle.fluid.proto import framework_pb2
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from paddle.fluid.framework import Program
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from paddle.fluid import debugger
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from google.protobuf import text_format
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import paddle.fluid as fluid
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from collections import OrderedDict
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from paddle.fluid import core
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import subprocess
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import os
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import numpy as np
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__all__ = ['UtilBase']
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class UtilFactory(object):
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def _create_util(self, context=None):
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util = UtilBase()
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if context is not None and "valid_strategy" in context:
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util._set_strategy(context["valid_strategy"])
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if context is not None and "role_maker" in context:
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util._set_role_maker(context["role_maker"])
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return util
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class UtilBase(object):
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def __init__(self):
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self.role_maker = None
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self.dist_strategy = None
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def _set_strategy(self, dist_strategy):
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self.dist_strategy = dist_strategy
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def _set_role_maker(self, role_maker):
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self.role_maker = role_maker
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def _set_file_system(self, fs_client):
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assert isinstance(
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fs_client, FS
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), "fs_client must be the instance of paddle.distributed.fleet.utils.FS"
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self.fs_client = fs_client
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def all_reduce(self, input, mode="sum", comm_world="worker"):
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"""
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All reduce `input` between specified collection. This is a distributed API.
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Args:
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input (list|numpy.array): The input variable to do all_reduce between specified collection.
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mode (str): "sum" or "min" or "max".
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comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
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Returns:
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output(Numpy.array|None): A numpy array with the same shape as the `input` .
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Examples:
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.. code-block:: python
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# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
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import paddle.distributed.fleet as fleet
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from paddle.distributed.fleet import PaddleCloudRoleMaker
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import sys
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import numpy as np
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import os
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os.environ["PADDLE_WITH_GLOO"] = "2"
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def train():
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role = PaddleCloudRoleMaker(
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is_collective=False,
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init_gloo=True,
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path="./tmp_gloo")
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fleet.init(role)
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if fleet.is_server():
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input = [1, 2]
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output = fleet.util.all_reduce(input, "sum", "server")
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print(output)
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# [2, 4]
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elif fleet.is_worker():
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input = np.array([3, 4])
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output = fleet.util.all_reduce(input, "sum", "worker")
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print(output)
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# [6, 8]
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output = fleet.util.all_reduce(input, "sum", "all")
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print(output)
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# [8, 12]
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if __name__ == "__main__":
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train()
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"""
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return self.role_maker._all_reduce(input, mode, comm_world)
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def barrier(self, comm_world="worker"):
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"""
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Barrier between specified collection.
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Args:
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comm_world (str, optional): Collection used to execute barrier operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
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Examples:
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.. code-block:: python
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# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
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import paddle.distributed.fleet as fleet
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from paddle.distributed.fleet import PaddleCloudRoleMaker
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import sys
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import os
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os.environ["PADDLE_WITH_GLOO"] = "2"
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def train():
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role = PaddleCloudRoleMaker(
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is_collective=False,
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init_gloo=True,
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path="./tmp_gloo")
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fleet.init(role)
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if fleet.is_server():
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fleet.util.barrier("server")
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print("all server arrive here")
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elif fleet.is_worker():
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fleet.util.barrier("worker")
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print("all server arrive here")
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fleet.util.barrier("all")
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print("all servers and workers arrive here")
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if __name__ == "__main__":
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train()
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"""
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self.role_maker._barrier(comm_world)
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def all_gather(self, input, comm_world="worker"):
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"""
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All gather `input` between specified collection.
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Args:
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input (Int|Float): The input variable to do all_gather between specified collection.
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comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
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Returns:
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output (List): A list of gathered values.
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Examples:
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.. code-block:: python
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# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
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import paddle.distributed.fleet as fleet
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from paddle.distributed.fleet import PaddleCloudRoleMaker
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import sys
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import os
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os.environ["PADDLE_WITH_GLOO"] = "2"
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def train():
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role = PaddleCloudRoleMaker(
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is_collective=False,
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init_gloo=True,
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path="./tmp_gloo")
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fleet.init(role)
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if fleet.is_server():
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input = fleet.server_index()
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output = fleet.util.all_gather(input, "server")
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print(output)
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# output = [0, 1]
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elif fleet.is_worker():
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input = fleet.worker_index()
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output = fleet.util.all_gather(input, "worker")
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# output = [0, 1]
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print(output)
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output = fleet.util.all_gather(input, "all")
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print(output)
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# output = [0, 1, 0, 1]
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if __name__ == "__main__":
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train()
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"""
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return self.role_maker._all_gather(input, comm_world)
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def _broadcast(self):
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pass
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def _scatter(self):
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pass
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def get_file_shard(self, files):
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"""
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Split files before distributed training, and return filelist assigned to the current trainer.
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.. code-block:: text
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example 1: files is [a, b, c ,d, e] and trainer_num = 2, then trainer
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0 gets [a, b, c] and trainer 1 gets [d, e].
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example 2: files is [a, b], and trainer_num = 3, then trainer 0 gets
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[a], trainer 1 gets [b], trainer 2 gets []
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Args:
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files(list): File list need to be read.
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Returns:
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List: Files belong to this worker.
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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from paddle.distributed.fleet import UserDefinedRoleMaker
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role = UserDefinedRoleMaker(
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is_collective=False,
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init_gloo=False,
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current_id=0,
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role=fleet.Role.WORKER,
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worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
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server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
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fleet.init(role)
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files = fleet.util.get_file_shard(["file1", "file2", "file3"])
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print(files)
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# files = ["file1", "file2"]
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"""
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if not isinstance(files, list):
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raise TypeError("files should be a list of file need to be read.")
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trainer_id = self.role_maker._worker_index()
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trainers = self.role_maker._worker_num()
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remainder = len(files) % trainers
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blocksize = int(len(files) / trainers)
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blocks = [blocksize] * trainers
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for i in range(remainder):
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blocks[i] += 1
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trainer_files = [[]] * trainers
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begin = 0
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for i in range(trainers):
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trainer_files[i] = files[begin:begin + blocks[i]]
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begin += blocks[i]
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return trainer_files[trainer_id]
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def print_on_rank(self, message, rank_id):
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"""
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Woker of rank `rank_id` print some message.
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Args:
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message(str): Log to be printed.
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rank_id(int): trainer id.
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Examples:
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.. code-block:: python
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import paddle.distributed.fleet as fleet
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from paddle.distributed.fleet import UserDefinedRoleMaker
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role = UserDefinedRoleMaker(
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is_collective=False,
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init_gloo=False,
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current_id=0,
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role=fleet.Role.WORKER,
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worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
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server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
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fleet.init(role)
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fleet.util.print_on_rank("I'm worker 0", 0)
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"""
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if self.role_maker._worker_index() != rank_id:
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return
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print(message)
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def _save_program(self, program, model_filename='__model__', is_text=False):
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if is_text:
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with open(model_filename, "w") as f:
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f.write(str(program))
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else:
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with open(model_filename, "wb") as f:
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f.write(program.desc.serialize_to_string())
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def _load_program(self, path, is_text):
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def load_program_binary(path):
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"""load program from binary string file"""
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with open(path, "rb") as f:
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program_desc_str = f.read()
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return Program.parse_from_string(program_desc_str)
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def load_program_text(path):
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"""load program from human-readable text file"""
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with open(path, "r") as f:
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program_desc_text = f.read()
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prog_desc = framework_pb2.ProgramDesc()
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text_format.Merge(program_desc_text, prog_desc)
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return Program.parse_from_string(prog_desc.SerializeToString())
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if is_text:
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return load_program_text(path)
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else:
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return load_program_binary(path)
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def _program_type_trans(self, prog_dir, prog_fn, is_text):
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prog = self._load_program(os.path.join(prog_dir, prog_fn), is_text)
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prog_out_fn = prog_fn + ".bin" if is_text else prog_fn + ".pbtxt"
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self._save_program(prog,
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os.path.join(prog_dir, prog_out_fn), 1 - is_text)
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return prog_out_fn
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def _visualize_graphviz(self, program, output_dir, output_filename):
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block = program.global_block()
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dot_path = os.path.join(output_dir, output_filename + '.dot')
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pdf_path = os.path.join(output_dir, output_filename + '.pdf')
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debugger.draw_block_graphviz(block, path=dot_path)
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cmd = ["dot", "-Tpdf", dot_path, "-o", pdf_path]
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p = subprocess.Popen(
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cmd,
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE)
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p.wait()
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def _proto_check(self, config):
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train_prog = self._load_program(config.train_prog_path,
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config.is_text_train_program)
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pruned_prog = self._load_program(config.pruned_prog_path,
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config.is_text_pruned_program)
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is_match = True
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pruned_vars = [(v.name, v) for v in pruned_prog.list_vars()
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if fluid.io.is_persistable(v)]
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pruned_vars = OrderedDict(pruned_vars)
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pruned_vars_name = [name for name in pruned_vars]
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print("persistable vars in pruned program: {}".format(pruned_vars_name))
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# feed and fetch op is added in pruned program when pruning, not need to be found in train program
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feed_fetch_type_list = [
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core.VarDesc.VarType.FEED_MINIBATCH, core.VarDesc.VarType.FETCH_LIST
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]
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for var_name in pruned_vars:
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var = pruned_vars[var_name]
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# feed and fetch op is added in pruned program when pruning, not need to be found in train program
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if var.type in feed_fetch_type_list:
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break
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try:
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train_prog_var = train_prog.global_block().var(var_name)
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except ValueError as e:
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print(
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"Not find variable '%s' in train program. please check pruning."
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% var_name)
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is_match = False
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continue
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if var.shape != train_prog_var.shape or var.dtype != train_prog_var.dtype:
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print(
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"variable: {} not match. in pruned program shape: {} dtype:{}, in train program shape: {} dtype: {}".
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format(var_name, var.shape, var.dtype, train_prog_var.shape,
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train_prog_var.dtype))
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is_match = False
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return is_match
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def _params_check(self, config):
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def feed_gen(batch_size, feeded_vars_dims, feeded_vars_filelist):
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def reader(batch_size, fn, dim):
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data = []
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if isinstance(dim, list) or isinstance(dim, tuple):
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shape = list(dim)
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_temp = 1
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for x in dim:
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_temp = _temp * x
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dim = _temp
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else:
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shape = [dim]
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shape = [batch_size] + shape
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dim = dim * batch_size
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for line in open(fn, 'r'):
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fields = line.strip().split(' ')
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fields = [float(d) for d in fields]
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while len(fields) >= dim:
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tmp = fields[:dim]
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fields = fields[dim:]
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data.append(np.array(tmp).reshape(shape))
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return data
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batch_feed = []
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for i, fn in enumerate(feeded_vars_filelist):
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batch_feed.append(reader(batch_size, fn, feeded_vars_dims[i]))
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return batch_feed
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prog = self._load_program(
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os.path.join(config.dump_model_dir, config.dump_program_filename),
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config.is_text_dump_program)
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if config.is_text_dump_program:
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model_filename = self._program_type_trans(
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config.dump_model_dir, config.dump_program_filename,
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config.is_text_dump_program)
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saved_params = [
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v for v in prog.list_vars() if fluid.io.is_persistable(v)
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]
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print("persistable vars in dump program: {}".format(
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[v.name for v in saved_params]))
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def check_not_expected_ops(prog, not_expected_op_types):
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op_types_set = set()
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for op in prog.global_block().ops:
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if op.type in not_expected_op_types and op.type not in op_types_set:
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op_types_set.add(op.type)
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return op_types_set
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not_expected_op_types = check_not_expected_ops(prog, ["lookup_table"])
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if len(not_expected_op_types) > 0:
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print(
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"find op type '{}' in program, please check if your program is pruned correctly !".
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format(list(not_expected_op_types)))
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return False
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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inference_program, feed_target_names, fetch_targets = \
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fluid.io.load_inference_model(config.dump_model_dir, exe, model_filename=model_filename,
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params_filename=config.save_params_filename)
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# check program vars and saved vars shape
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orig_para_shape = {
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each_var.name: tuple(each_var.desc.shape())
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for each_var in saved_params
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}
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for each_var in saved_params:
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var_temp = fluid.global_scope().find_var(each_var.name)
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assert var_temp != None, "can't not find var: " + each_var.name
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new_shape = (np.array(var_temp.get_tensor())).shape
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assert each_var.name in orig_para_shape, each_var.name + "MUST in var list"
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orig_shape = orig_para_shape.get(each_var.name)
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if new_shape != orig_shape:
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raise RuntimeError(
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"Shape not matching: the Program requires a parameter with a shape of ({}), "
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"while the loaded parameter (namely [ {} ]) has a shape of ({}).".
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format(orig_shape, each_var.name, new_shape))
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# check feed/fetch vars in program and config
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feed_config = config.feed_config
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fetch_config = config.fetch_config
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fetch_targets_names = [v.name for v in fetch_targets]
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if not feed_target_names:
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print("warning! no feed targets in program.")
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if not fetch_targets_names:
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print("warning! no fetch targets in program.")
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fetch_list = fetch_targets
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feed_name_list = feed_target_names
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if feed_config.feeded_vars_names is not None and feed_target_names != feed_config.feeded_vars_names:
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print(
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"warning! feed vars in program and config are diff: feed in program: {}. feed in config {}.".
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format(feed_target_names, feed_config.feeded_vars_names))
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feed_name_list = feed_config.feeded_vars_names
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# remove feed op in inference_program. new feed op will be added in exe.run
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global_block = inference_program.global_block()
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need_to_remove_op_index = []
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for i, op in enumerate(global_block.ops):
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op.desc.set_is_target(False)
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if op.type == "feed": # only remove feed op here
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need_to_remove_op_index.append(i)
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for index in need_to_remove_op_index[::-1]:
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global_block._remove_op(index)
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if fetch_config.fetch_vars_names is not None and fetch_targets_names != fetch_config.fetch_vars_names:
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print(
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"warning! fetch vars in program and config are diff: fetch in program: {}. fetch in config {}.".
|
|
format(fetch_targets_names, fetch_config.fetch_vars_names))
|
|
fetch_list = [
|
|
inference_program.global_block().var(i)
|
|
for i in fetch_config.fetch_vars_names
|
|
]
|
|
# remove fetch op in inference_program. new fetch op will be added in exe.run
|
|
global_block = inference_program.global_block()
|
|
need_to_remove_op_index = []
|
|
for i, op in enumerate(global_block.ops):
|
|
op.desc.set_is_target(False)
|
|
if op.type == "fetch": # only remove fetch op here
|
|
need_to_remove_op_index.append(i)
|
|
for index in need_to_remove_op_index[::-1]:
|
|
global_block._remove_op(index)
|
|
|
|
# if fetch_list have lod tensor
|
|
return_numpy = all([v.lod_level == 0 for v in fetch_list])
|
|
|
|
# try dump fetch_targets
|
|
feed_tensors = []
|
|
assert len(feed_config.feeded_vars_names) == len(
|
|
feed_config.feeded_vars_dims) == len(
|
|
feed_config.feeded_vars_types)
|
|
# check program vars and feed tensor shape in config
|
|
for i in range(len(feed_config.feeded_vars_names)):
|
|
var = inference_program.global_block().var(
|
|
feed_config.feeded_vars_names[i])
|
|
if not isinstance(feed_config.feeded_vars_dims[i],
|
|
(list, tuple)):
|
|
tensor_shape = (feed_config.feeded_vars_dims[i], )
|
|
else:
|
|
tensor_shape = tuple(feed_config.feeded_vars_dims[i])
|
|
feed_config.feeded_vars_dims[i] = tensor_shape
|
|
var_shape = var.shape[1:]
|
|
if tensor_shape != var_shape:
|
|
raise RuntimeError(
|
|
"feed variable '{}' shape not match. infer program shape: {}. feed tensor shape: {}".
|
|
format(feed_config.feeded_vars_names[i], var_shape,
|
|
tensor_shape))
|
|
|
|
if not feed_config.feeded_vars_filelist:
|
|
print("generate random feed vars.")
|
|
for i in range(len(feed_config.feeded_vars_names)):
|
|
var = inference_program.global_block().var(
|
|
feed_config.feeded_vars_names[i])
|
|
# create fake feed tensor. if lod_level > 1, should create_lod_tensor()
|
|
if var.lod_level == 0:
|
|
feed_tensors.append(
|
|
np.array(
|
|
np.random.random(
|
|
tuple([config.batch_size] + list(
|
|
feed_config.feeded_vars_dims[i]))),
|
|
dtype=feed_config.feeded_vars_types[i]))
|
|
elif var.lod_level == 1:
|
|
t = np.array(
|
|
np.random.random(
|
|
tuple([config.batch_size] + list(
|
|
feed_config.feeded_vars_dims[i]))),
|
|
dtype=feed_config.feeded_vars_types[i])
|
|
feed_tensors.append(
|
|
fluid.create_lod_tensor(t, [[1] * config.batch_size
|
|
], place))
|
|
else:
|
|
raise RuntimeError(
|
|
"vars with lod_level >= 2 is not supported now in this infer program check tool."
|
|
)
|
|
results = exe.run(inference_program,
|
|
feed={
|
|
name: feed_tensors[i]
|
|
for i, name in enumerate(feed_name_list)
|
|
},
|
|
fetch_list=fetch_list,
|
|
return_numpy=return_numpy)
|
|
else:
|
|
print("load feed vars from files: {}.".format(
|
|
feed_config.feeded_vars_filelist))
|
|
feed_vars = [
|
|
inference_program.global_block().var(
|
|
feed_config.feeded_vars_names[i])
|
|
for i in range(len(feed_config.feeded_vars_names))
|
|
]
|
|
feeder = fluid.DataFeeder(feed_list=feed_vars, place=place)
|
|
batch_feed = feed_gen(config.batch_size,
|
|
feed_config.feeded_vars_dims,
|
|
feed_config.feeded_vars_filelist)
|
|
slots = [batch_feed]
|
|
results = exe.run(inference_program,
|
|
feed=feeder.feed(slots),
|
|
fetch_list=fetch_list,
|
|
return_numpy=return_numpy)
|
|
for i, v in enumerate(fetch_list):
|
|
print("fetch_targets name: %s" % v.name)
|
|
print("fetch_targets: {}".format(results[i]))
|
|
return results
|