dmlc--dgl
Da Zheng
75ffc31f76
* add doc. * update DistGraph. * add DistTensor. * update DistEmbedding. * add partition.py * add sampling. * fix. * add graph partition book and create a base class. * fix test. * add rst. * update doc rst. * update. * fix. * fix docs * update distributed tensor and embeddings. * add checks. * update DistGraph. * update initialization. * fix graph partition book. * update graph partition book. * update partition. * update partition. * fix. * add example code. * update DistGraph * Update python/dgl/distributed/dist_context.py Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com> * Update python/dgl/distributed/dist_context.py Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com> * Update python/dgl/distributed/dist_dataloader.py Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com> * Update python/dgl/distributed/dist_dataloader.py Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com> * Update python/dgl/distributed/dist_dataloader.py Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com> * update initialize. * update dataloader. * update distgraph. * update DistGraph. * update DistTensor. * update. * more updates. * fix lint. * add num_nodes and num_edges Co-authored-by: Chao Ma <mctt90@gmail.com> Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com> Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
354 行
14 KiB
Python
354 行
14 KiB
Python
import os
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import time
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import numpy as np
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import socket
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from scipy import sparse as spsp
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import dgl
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import backend as F
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import unittest
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from dgl.graph_index import create_graph_index
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import multiprocessing as mp
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from numpy.testing import assert_array_equal
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if os.name != 'nt':
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import fcntl
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import struct
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def get_local_usable_addr():
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"""Get local usable IP and port
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Returns
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-------
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str
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IP address, e.g., '192.168.8.12:50051'
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"""
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sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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try:
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# doesn't even have to be reachable
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sock.connect(('10.255.255.255', 1))
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ip_addr = sock.getsockname()[0]
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except ValueError:
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ip_addr = '127.0.0.1'
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finally:
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sock.close()
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sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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sock.bind(("", 0))
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sock.listen(1)
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port = sock.getsockname()[1]
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sock.close()
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return ip_addr + ' ' + str(port)
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# Create an one-part Graph
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node_map = F.tensor([0,0,0,0,0,0], F.int64)
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edge_map = F.tensor([0,0,0,0,0,0,0], F.int64)
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global_nid = F.tensor([0,1,2,3,4,5], F.int64)
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global_eid = F.tensor([0,1,2,3,4,5,6], F.int64)
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g = dgl.DGLGraph()
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g.add_nodes(6)
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g.add_edges(0, 1) # 0
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g.add_edges(0, 2) # 1
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g.add_edges(0, 3) # 2
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g.add_edges(2, 3) # 3
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g.add_edges(1, 1) # 4
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g.add_edges(0, 4) # 5
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g.add_edges(2, 5) # 6
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g.ndata[dgl.NID] = global_nid
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g.edata[dgl.EID] = global_eid
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gpb = dgl.distributed.graph_partition_book.BasicPartitionBook(part_id=0,
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num_parts=1,
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node_map=node_map,
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edge_map=edge_map,
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part_graph=g)
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node_policy = dgl.distributed.PartitionPolicy(policy_str='node',
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partition_book=gpb)
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edge_policy = dgl.distributed.PartitionPolicy(policy_str='edge',
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partition_book=gpb)
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data_0 = F.tensor([[1.,1.],[1.,1.],[1.,1.],[1.,1.],[1.,1.],[1.,1.]], F.float32)
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data_0_1 = F.tensor([1.,2.,3.,4.,5.,6.], F.float32)
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data_0_2 = F.tensor([1,2,3,4,5,6], F.int32)
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data_0_3 = F.tensor([1,2,3,4,5,6], F.int64)
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data_1 = F.tensor([[2.,2.],[2.,2.],[2.,2.],[2.,2.],[2.,2.],[2.,2.],[2.,2.]], F.float32)
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data_2 = F.tensor([[0.,0.],[0.,0.],[0.,0.],[0.,0.],[0.,0.],[0.,0.]], F.float32)
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def init_zero_func(shape, dtype):
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return F.zeros(shape, dtype, F.cpu())
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def udf_push(target, name, id_tensor, data_tensor):
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target[name][id_tensor] = data_tensor * data_tensor
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def add_push(target, name, id_tensor, data_tensor):
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target[name][id_tensor] += data_tensor
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@unittest.skipIf(os.name == 'nt' or os.getenv('DGLBACKEND') == 'tensorflow', reason='Do not support windows and TF yet')
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def test_partition_policy():
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assert node_policy.policy_str == 'node'
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assert edge_policy.policy_str == 'edge'
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assert node_policy.part_id == 0
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assert edge_policy.part_id == 0
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local_nid = node_policy.to_local(F.tensor([0,1,2,3,4,5]))
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local_eid = edge_policy.to_local(F.tensor([0,1,2,3,4,5,6]))
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assert_array_equal(F.asnumpy(local_nid), F.asnumpy(F.tensor([0,1,2,3,4,5], F.int64)))
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assert_array_equal(F.asnumpy(local_eid), F.asnumpy(F.tensor([0,1,2,3,4,5,6], F.int64)))
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nid_partid = node_policy.to_partid(F.tensor([0,1,2,3,4,5], F.int64))
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eid_partid = edge_policy.to_partid(F.tensor([0,1,2,3,4,5,6], F.int64))
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assert_array_equal(F.asnumpy(nid_partid), F.asnumpy(F.tensor([0,0,0,0,0,0], F.int64)))
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assert_array_equal(F.asnumpy(eid_partid), F.asnumpy(F.tensor([0,0,0,0,0,0,0], F.int64)))
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assert node_policy.get_part_size() == len(node_map)
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assert edge_policy.get_part_size() == len(edge_map)
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def start_server(server_id, num_clients, num_servers):
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# Init kvserver
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print("Sleep 5 seconds to test client re-connect.")
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time.sleep(5)
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kvserver = dgl.distributed.KVServer(server_id=server_id,
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ip_config='kv_ip_config.txt',
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num_servers=num_servers,
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num_clients=num_clients)
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kvserver.add_part_policy(node_policy)
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kvserver.add_part_policy(edge_policy)
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if kvserver.is_backup_server():
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kvserver.init_data('data_0', 'node')
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kvserver.init_data('data_0_1', 'node')
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kvserver.init_data('data_0_2', 'node')
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kvserver.init_data('data_0_3', 'node')
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else:
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kvserver.init_data('data_0', 'node', data_0)
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kvserver.init_data('data_0_1', 'node', data_0_1)
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kvserver.init_data('data_0_2', 'node', data_0_2)
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kvserver.init_data('data_0_3', 'node', data_0_3)
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# start server
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server_state = dgl.distributed.ServerState(kv_store=kvserver, local_g=None, partition_book=None)
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dgl.distributed.start_server(server_id=server_id,
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ip_config='kv_ip_config.txt',
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num_servers=num_servers,
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num_clients=num_clients,
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server_state=server_state)
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def start_server_mul_role(server_id, num_clients, num_servers):
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# Init kvserver
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kvserver = dgl.distributed.KVServer(server_id=server_id,
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ip_config='kv_ip_mul_config.txt',
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num_servers=num_servers,
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num_clients=num_clients)
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kvserver.add_part_policy(node_policy)
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if kvserver.is_backup_server():
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kvserver.init_data('data_0', 'node')
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else:
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kvserver.init_data('data_0', 'node', data_0)
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# start server
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server_state = dgl.distributed.ServerState(kv_store=kvserver, local_g=None, partition_book=None)
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dgl.distributed.start_server(server_id=server_id,
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ip_config='kv_ip_mul_config.txt',
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num_servers=num_servers,
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num_clients=num_clients,
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server_state=server_state)
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def start_client(num_clients, num_servers):
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os.environ['DGL_DIST_MODE'] = 'distributed'
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# Note: connect to server first !
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dgl.distributed.initialize(ip_config='kv_ip_config.txt', num_servers=num_servers)
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# Init kvclient
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kvclient = dgl.distributed.KVClient(ip_config='kv_ip_config.txt', num_servers=num_servers)
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kvclient.map_shared_data(partition_book=gpb)
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assert dgl.distributed.get_num_client() == num_clients
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kvclient.init_data(name='data_1',
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shape=F.shape(data_1),
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dtype=F.dtype(data_1),
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part_policy=edge_policy,
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init_func=init_zero_func)
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kvclient.init_data(name='data_2',
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shape=F.shape(data_2),
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dtype=F.dtype(data_2),
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part_policy=node_policy,
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init_func=init_zero_func)
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# Test data_name_list
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name_list = kvclient.data_name_list()
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print(name_list)
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assert 'data_0' in name_list
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assert 'data_0_1' in name_list
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assert 'data_0_2' in name_list
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assert 'data_0_3' in name_list
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assert 'data_1' in name_list
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assert 'data_2' in name_list
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# Test get_meta_data
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meta = kvclient.get_data_meta('data_0')
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dtype, shape, policy = meta
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assert dtype == F.dtype(data_0)
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assert shape == F.shape(data_0)
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assert policy.policy_str == 'node'
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meta = kvclient.get_data_meta('data_0_1')
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dtype, shape, policy = meta
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assert dtype == F.dtype(data_0_1)
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assert shape == F.shape(data_0_1)
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assert policy.policy_str == 'node'
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meta = kvclient.get_data_meta('data_0_2')
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dtype, shape, policy = meta
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assert dtype == F.dtype(data_0_2)
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assert shape == F.shape(data_0_2)
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assert policy.policy_str == 'node'
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meta = kvclient.get_data_meta('data_0_3')
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dtype, shape, policy = meta
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assert dtype == F.dtype(data_0_3)
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assert shape == F.shape(data_0_3)
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assert policy.policy_str == 'node'
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meta = kvclient.get_data_meta('data_1')
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dtype, shape, policy = meta
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assert dtype == F.dtype(data_1)
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assert shape == F.shape(data_1)
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assert policy.policy_str == 'edge'
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meta = kvclient.get_data_meta('data_2')
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dtype, shape, policy = meta
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assert dtype == F.dtype(data_2)
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assert shape == F.shape(data_2)
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assert policy.policy_str == 'node'
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# Test push and pull
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id_tensor = F.tensor([0,2,4], F.int64)
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data_tensor = F.tensor([[6.,6.],[6.,6.],[6.,6.]], F.float32)
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kvclient.push(name='data_0',
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id_tensor=id_tensor,
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data_tensor=data_tensor)
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kvclient.push(name='data_1',
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id_tensor=id_tensor,
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data_tensor=data_tensor)
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kvclient.push(name='data_2',
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id_tensor=id_tensor,
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data_tensor=data_tensor)
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res = kvclient.pull(name='data_0', id_tensor=id_tensor)
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assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
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res = kvclient.pull(name='data_1', id_tensor=id_tensor)
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assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
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res = kvclient.pull(name='data_2', id_tensor=id_tensor)
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assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
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# Register new push handler
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kvclient.register_push_handler('data_0', udf_push)
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kvclient.register_push_handler('data_1', udf_push)
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kvclient.register_push_handler('data_2', udf_push)
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# Test push and pull
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kvclient.push(name='data_0',
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id_tensor=id_tensor,
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data_tensor=data_tensor)
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kvclient.push(name='data_1',
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id_tensor=id_tensor,
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data_tensor=data_tensor)
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kvclient.push(name='data_2',
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id_tensor=id_tensor,
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data_tensor=data_tensor)
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kvclient.barrier()
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data_tensor = data_tensor * data_tensor
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res = kvclient.pull(name='data_0', id_tensor=id_tensor)
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assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
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res = kvclient.pull(name='data_1', id_tensor=id_tensor)
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assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
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res = kvclient.pull(name='data_2', id_tensor=id_tensor)
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assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
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# Test delete data
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kvclient.delete_data('data_0')
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kvclient.delete_data('data_1')
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kvclient.delete_data('data_2')
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# Register new push handler
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kvclient.init_data(name='data_3',
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shape=F.shape(data_2),
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dtype=F.dtype(data_2),
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part_policy=node_policy,
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init_func=init_zero_func)
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kvclient.register_push_handler('data_3', add_push)
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data_tensor = F.tensor([[6.,6.],[6.,6.],[6.,6.]], F.float32)
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kvclient.barrier()
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time.sleep(kvclient.client_id + 1)
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print("add...")
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kvclient.push(name='data_3',
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id_tensor=id_tensor,
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data_tensor=data_tensor)
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kvclient.barrier()
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res = kvclient.pull(name='data_3', id_tensor=id_tensor)
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data_tensor = data_tensor * num_clients
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assert_array_equal(F.asnumpy(res), F.asnumpy(data_tensor))
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def start_client_mul_role(i, num_workers, num_servers):
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os.environ['DGL_DIST_MODE'] = 'distributed'
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# Initialize creates kvstore !
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dgl.distributed.initialize(ip_config='kv_ip_mul_config.txt', num_servers=num_servers, num_workers=num_workers)
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if i == 0: # block one trainer
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time.sleep(5)
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kvclient = dgl.distributed.kvstore.get_kvstore()
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kvclient.barrier()
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print("i: %d role: %s" % (i, kvclient.role))
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assert dgl.distributed.role.get_num_trainers() == 2
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assert dgl.distributed.role.get_trainer_rank() < 2
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print('trainer rank: %d, global rank: %d' % (dgl.distributed.role.get_trainer_rank(),
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dgl.distributed.role.get_global_rank()))
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dgl.distributed.exit_client()
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@unittest.skipIf(os.name == 'nt' or os.getenv('DGLBACKEND') == 'tensorflow', reason='Do not support windows and TF yet')
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def test_kv_store():
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ip_config = open("kv_ip_config.txt", "w")
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num_servers = 2
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num_clients = 2
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ip_addr = get_local_usable_addr()
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ip_config.write('{}\n'.format(ip_addr))
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ip_config.close()
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ctx = mp.get_context('spawn')
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pserver_list = []
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pclient_list = []
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for i in range(num_servers):
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pserver = ctx.Process(target=start_server, args=(i, num_clients, num_servers))
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pserver.start()
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pserver_list.append(pserver)
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for i in range(num_clients):
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pclient = ctx.Process(target=start_client, args=(num_clients, num_servers))
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pclient.start()
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pclient_list.append(pclient)
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for i in range(num_clients):
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pclient_list[i].join()
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for i in range(num_servers):
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pserver_list[i].join()
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@unittest.skipIf(os.name == 'nt' or os.getenv('DGLBACKEND') == 'tensorflow', reason='Do not support windows and TF yet')
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def test_kv_multi_role():
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ip_config = open("kv_ip_mul_config.txt", "w")
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num_servers = 2
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num_trainers = 2
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num_samplers = 2
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# There are two trainer processes and each trainer process has two sampler processes.
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num_clients = num_trainers * (1 + num_samplers)
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ip_addr = get_local_usable_addr()
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ip_config.write('{}\n'.format(ip_addr))
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ip_config.close()
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ctx = mp.get_context('spawn')
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pserver_list = []
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pclient_list = []
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for i in range(num_servers):
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pserver = ctx.Process(target=start_server_mul_role, args=(i, num_clients, num_servers))
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pserver.start()
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pserver_list.append(pserver)
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for i in range(num_trainers):
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pclient = ctx.Process(target=start_client_mul_role, args=(i, num_samplers, num_servers))
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pclient.start()
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pclient_list.append(pclient)
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for i in range(num_trainers):
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pclient_list[i].join()
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for i in range(num_servers):
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pserver_list[i].join()
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if __name__ == '__main__':
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test_partition_policy()
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test_kv_store()
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test_kv_multi_role()
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