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244 lines
8.6 KiB
244 lines
8.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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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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""" test Communicate """
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import _executor
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from mindspore.communication._comm_helper import Backend
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from mindspore.communication.management import HCCL_WORLD_COMM_GROUP, NCCL_WORLD_COMM_GROUP, GlobalComm, init
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from mindspore.nn import Dense
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from mindspore.nn import Momentum
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from mindspore.nn import ReLU
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.ops.operations.comm_ops import AllReduce, AllGather, _AlltoAll, ReduceOp, ReduceScatter
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from mindspore.ops.operations.comm_ops import Broadcast, AllSwap
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from mindspore.ops.operations.array_ops import Gather
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import mindspore
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# pylint: disable=W0212
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# W0212: protected-access
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tag = 0
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context.set_context(device_target="Ascend")
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init("hccl")
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class AllReduceNet(nn.Cell):
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"""AllReduceNet definition"""
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def __init__(self, input_channel, out_channel, op):
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super(AllReduceNet, self).__init__()
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self.dense = Dense(input_channel, out_channel)
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self.reduce = AllReduce(op)
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self.relu = ReLU()
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def construct(self, x):
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x = self.dense(x)
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x = self.reduce(x)
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return self.relu(x)
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class BroadCastNet(nn.Cell):
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"""BroadCastNet definition"""
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def __init__(self, input_channel, out_channel):
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super(BroadCastNet, self).__init__()
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self.dense = Dense(input_channel, out_channel)
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self.broadcast = Broadcast(0)
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def construct(self, x):
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x, = self.broadcast((x,))
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x = self.dense(x)
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return x
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class AllGatherNet(nn.Cell):
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"""AllGatherNet definition"""
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def __init__(self, input_channel, out_channel):
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super(AllGatherNet, self).__init__()
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self.dense = Dense(input_channel, out_channel)
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if GlobalComm.BACKEND is Backend.HCCL:
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self.allgather = AllGather(group=HCCL_WORLD_COMM_GROUP)
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elif GlobalComm.BACKEND is Backend.NCCL:
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self.allgather = AllGather(group=NCCL_WORLD_COMM_GROUP)
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else:
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self.allgather = AllGather()
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self.relu = ReLU()
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def construct(self, x):
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x = self.dense(x)
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x = self.allgather(x)
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return self.relu(x)
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class ReduceScatterNet(nn.Cell):
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"""ReduceScatterNet definition"""
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def __init__(self, input_channel, out_channel, op):
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super(ReduceScatterNet, self).__init__()
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self.dense = Dense(input_channel, out_channel)
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self.reducescatter = ReduceScatter(op)
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self.relu = ReLU()
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def construct(self, x):
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x = self.dense(x)
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x = self.reducescatter(x)
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return self.relu(x)
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class AlltoAllNet(nn.Cell):
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"""AlltoAllNet definition"""
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def __init__(self, input_channel, out_channel):
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super(AlltoAllNet, self).__init__()
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self.dense = Dense(input_channel, out_channel)
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self.alltoall = _AlltoAll(1, 0, 1)
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self.relu = ReLU()
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def construct(self, x):
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x = self.dense(x)
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x = self.alltoall(x)
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return self.relu(x)
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class AllSwapNet(nn.Cell):
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"""AlltoAllNet definition"""
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def __init__(self, batch_size, input_channel, out_channel):
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super(AllSwapNet, self).__init__()
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self.dense = Dense(input_channel, out_channel)
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self.allswap = AllSwap()
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self.relu = ReLU()
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part_slice = batch_size / 2
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self.send_size = Tensor([0, part_slice*out_channel, part_slice*out_channel], mindspore.int64)
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self.recv_size = Tensor([part_slice*out_channel, part_slice*out_channel, 0], mindspore.int64)
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self.gatherv2 = Gather()
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self.input = Tensor(np.ones([1]), mindspore.int32)
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def construct(self, x):
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x = self.allswap(x, self.send_size, self.recv_size)
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x = self.relu(x)
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x = self.gatherv2(x, self.input, 0)
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return x
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def run_allreduce(op):
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"""run_allreduce"""
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context.set_context(mode=context.GRAPH_MODE)
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input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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label_tensor = Tensor(np.array([[1.2], [2.2]], dtype=np.float32))
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network = AllReduceNet(2, 1, op)
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
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learning_rate=0.1,
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momentum=0.9)
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network = WithLossCell(network, loss_fn)
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network = TrainOneStepCell(network, optimizer)
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_executor.compile(network, input_tensor, label_tensor)
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def test_allreduce():
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"""test_allreduce"""
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context.set_context(mode=context.GRAPH_MODE)
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run_allreduce(ReduceOp.SUM)
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run_allreduce(ReduceOp.MAX)
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run_allreduce(ReduceOp.MIN)
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run_allreduce(ReduceOp.PROD)
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def test_allgather():
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"""test_allgather"""
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context.set_context(mode=context.GRAPH_MODE)
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input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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label_tensor = Tensor(np.array([[1.2], [2.2]], dtype=np.float32))
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network = AllGatherNet(2, 1)
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
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learning_rate=0.1,
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momentum=0.9)
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network = WithLossCell(network, loss_fn)
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network = TrainOneStepCell(network, optimizer)
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_executor.compile(network, input_tensor, label_tensor)
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def test_allswap():
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"""run_allswap"""
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context.set_context(mode=context.GRAPH_MODE)
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input_tensor = Tensor(np.ones((100, 20)), dtype=mindspore.float32)
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label_tensor = Tensor(np.ones((1, 20)), dtype=mindspore.float32)
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network = AllSwapNet(100, 20, 20)
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
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learning_rate=0.1,
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momentum=0.9)
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network = WithLossCell(network, loss_fn)
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network = TrainOneStepCell(network, optimizer)
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_executor.compile(network, input_tensor, label_tensor)
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def run_reducescatter(op):
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"""run_reducescatter"""
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context.set_context(mode=context.GRAPH_MODE)
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input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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label_tensor = Tensor(np.array([[1.2], [2.2]], dtype=np.float32))
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network = ReduceScatterNet(2, 1, op)
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
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learning_rate=0.1,
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momentum=0.9)
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network = WithLossCell(network, loss_fn)
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network = TrainOneStepCell(network, optimizer)
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_executor.compile(network, input_tensor, label_tensor)
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def test_reducescatter():
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"""test_reducescatter"""
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context.set_context(mode=context.GRAPH_MODE)
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run_reducescatter(ReduceOp.SUM)
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def test_broadcast():
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"""test_broadcast"""
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context.set_context(mode=context.GRAPH_MODE)
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input_tensor_1 = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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label_tensor = Tensor(np.array([[1.2], [2.2]], dtype=np.float32))
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network = BroadCastNet(2, 1)
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
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learning_rate=0.1,
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momentum=0.9)
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network = WithLossCell(network, loss_fn)
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network = TrainOneStepCell(network, optimizer)
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_executor.compile(network, input_tensor_1, label_tensor)
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def test_alltoall():
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"""test_alltoall"""
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context.set_context(mode=context.GRAPH_MODE)
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input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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label_tensor = Tensor(np.array([[1.2], [2.2]], dtype=np.float32))
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network = AlltoAllNet(2, 1)
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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optimizer = Momentum(filter(lambda x: x.requires_grad, network.get_parameters()),
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learning_rate=0.1,
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momentum=0.9)
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network = WithLossCell(network, loss_fn)
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network = TrainOneStepCell(network, optimizer)
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_executor.compile(network, input_tensor, label_tensor)
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