wehub-resource-sync
144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
# Copyright (c) 2023 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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import os
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import test_collective_api_base as test_base
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import paddle
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import paddle.distributed as dist
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from paddle import base, framework
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from paddle.base import data_feeder
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paddle.enable_static()
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def all_gather_new(tensor_list, tensor, group=None):
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op_type = 'all_gather'
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helper = framework.LayerHelper(op_type, **locals())
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out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
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for elem in tensor_list:
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data_feeder.check_variable_and_dtype(
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elem,
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'tensor_list',
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[
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'float16',
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'float32',
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'float64',
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'int32',
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'int64',
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],
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op_type,
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)
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data_feeder.check_variable_and_dtype(
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tensor,
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'tensor',
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[
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'float16',
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'float32',
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'float64',
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'int32',
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'int64',
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],
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op_type,
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)
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ring_id = 0 if group is None else group.id
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nranks = dist.get_world_size()
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helper.append_op(
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type=op_type,
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inputs={'x': [tensor]},
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outputs={'out': [out]},
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attrs={
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'ring_id': ring_id,
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'nranks': nranks,
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},
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)
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tensor_list.clear()
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tensor_list.extend(paddle.split(out, nranks, 0))
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class TestCollectiveAllgatherAPI(test_base.TestCollectiveAPIRunnerBase):
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def __init__(self):
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self.global_ring_id = 0
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def get_model(self, main_prog, startup_program, rank, dtype="float32"):
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with base.program_guard(main_prog, startup_program):
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tensor_list = []
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tindata = paddle.static.data(
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name="tindata", shape=[10, 1000], dtype=dtype
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)
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paddle.distributed.all_gather(tensor_list, tindata)
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return tensor_list
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def get_model_new(
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self, main_prog, startup_program, rank, dtype=None, reduce_type=None
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):
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with base.program_guard(main_prog, startup_program):
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tensor_list = []
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tindata = paddle.static.data(
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name="tindata", shape=[10, 1000], dtype=dtype
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)
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all_gather_new(tensor_list, tindata)
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return tensor_list
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def run_trainer(self, args):
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train_prog = base.Program()
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startup_prog = base.Program()
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endpoints = args["endpoints"].split(",")
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rank = args["trainerid"]
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current_endpoint = args["currentendpoint"]
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nranks = 2
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paddle.distributed.collective._init_parallel_env(args["backend"])
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if args['backend'] == 'nccl':
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device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
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place = base.CUDAPlace(
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device_id
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) # if args.use_gpu else base.CPUPlace()
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elif args['backend'] == 'bkcl':
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device_id = int(os.getenv("FLAGS_selected_xpus", "0"))
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place = base.XPUPlace(device_id)
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else:
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place = base.CPUPlace()
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indata = test_base.create_test_data(
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shape=(10, 1000), dtype=args["dtype"], seed=os.getpid()
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)
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assert args['static_mode'] == 1, (
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"collective_allgather_api only support static graph mode"
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)
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result = (
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self.get_model_new(
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train_prog, startup_prog, rank, dtype=args["dtype"]
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)
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if args["use_comm_context"]
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else self.get_model(
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train_prog, startup_prog, rank, dtype=args["dtype"]
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)
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)
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exe = base.Executor(place)
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exe.run(startup_prog)
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fetch_list = []
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for elem in result:
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fetch_list.append(elem.name)
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out = exe.run(
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train_prog, feed={'tindata': indata}, fetch_list=fetch_list
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)
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test_base.dump_output(out)
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if __name__ == "__main__":
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test_base.runtime_main(TestCollectiveAllgatherAPI, "allgather")
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