cleanup (#30646)

cleanup test_ascend_group.py

python/paddle/fluid/tests/unittests/test_ascend_group.py

-# Copyright (c) 2019 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 os
-import sys
-import time
-import paddle.fluid as fluid
-from paddle.fluid import unique_name
-import paddle.fluid.core as core
-import paddle
-from paddle.fluid.layer_helper import LayerHelper
-
-paddle.enable_static()
-
-OpRole = core.op_proto_and_checker_maker.OpRole
-OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName()
-OP_ROLE_VAR_KEY = core.op_proto_and_checker_maker.kOpRoleVarAttrName()
-
-def init_communicator(startup_program, main_program, current_endpoint, endpoints, ring_id):
-    nranks = len(endpoints)
-    other_endpoints = endpoints[:]
-    other_endpoints.remove(current_endpoint)
-    group_rank=endpoints.index(current_endpoint)
-    assert group_rank >=0
-
-    block = startup_program.global_block()
-    nccl_id_var = block.create_var(
-        name=unique_name.generate('nccl_id'),
-        persistable=True,
-        type=core.VarDesc.VarType.RAW)
-    block.append_op(
-        type='c_gen_nccl_id',
-        inputs={},
-        outputs={'Out': nccl_id_var},
-        attrs={
-            'rank': group_rank,
-            'endpoint': current_endpoint,
-            'other_endpoints': other_endpoints,
-            OP_ROLE_KEY: OpRole.Forward,
-        })
-    block.append_op(
-        type='c_comm_init',
-        inputs={'X': nccl_id_var},
-        outputs={},
-        attrs={
-            'nranks': nranks,
-            'rank': group_rank,
-            'ring_id': ring_id,
-            OP_ROLE_KEY: OpRole.Forward,
-        })
-    block.create_var(
-        name="data",
-        persistable=True,
-        dtype='float32')
-
-    with fluid.program_guard(main_program):
-        op_type="c_allreduce_sum"
-        data=fluid.layers.fill_constant(shape=[1], dtype='float32', value=2.5)
-        helper = LayerHelper(op_type, **locals())
-        helper.append_op(
-            type=op_type,
-            inputs={'X': [data]},
-            outputs={'Out': [data]},
-            attrs={'ring_id': ring_id,
-                   'use_calc_stream': True})
-
-def train():
-    startup_programs=[]
-    main_programs=[]
-
-
-    trainer_endpoints=["127.0.0.1:6071","127.0.0.1:6072","127.0.0.1:6073","127.0.0.1:6074"]
-    groups=[[], [], []]
-    groups[0]=[trainer_endpoints[0], trainer_endpoints[1]]
-    groups[1]=[trainer_endpoints[2], trainer_endpoints[3]]
-    groups[2]=[trainer_endpoints[0], trainer_endpoints[2]]
-
-    for i in range(len(trainer_endpoints)):
-        startup_programs.append(fluid.Program())
-        main_programs.append(fluid.Program())
-
-    for idx, group in enumerate(groups):
-        for te in group:
-            te_idx = trainer_endpoints.index(te)
-            startup_program = startup_programs[te_idx]
-            main_program=main_programs[te_idx]
-            init_communicator(startup_program, main_program, te, group, idx)
-
-    print(len(startup_programs))
-    print(startup_programs[0])
-    print(main_programs[0])
-
-train()