511 lines
18 KiB
Python
511 lines
18 KiB
Python
# Copyright 2021 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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# ============================================================================
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import pytest
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import numpy as np
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import mindspore as ms
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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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from mindspore.ops.operations.comm_ops import NeighborExchange
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_w1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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_x1 = Tensor(np.ones([32, 16]), dtype=ms.float32)
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_x2 = Tensor(np.ones([16, 32]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(mode=context.GRAPH_MODE)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_train()
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_cell_graph_executor.compile(train_net, _x1, _x2)
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def test_NeighborExchange_two_inputs_success():
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"""
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Feature: NeighborExchange
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Description: two inputs and two outputs, with valid arguments
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Expectation: success
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class MatMulNet(nn.Cell):
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def __init__(self, weight1):
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super(MatMulNet, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0, 1], recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 32], [32, 16]), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out, x1))
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return out[0]
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net = MatMulNet(_w1)
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compile_net(net)
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def test_NeighborExchange_single_input_success():
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"""
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Feature: NeighborExchange
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Description: one inputs and two outputs, with valid arguments
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Expectation: success
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class MatMulNet2(nn.Cell):
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def __init__(self, weight1):
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super(MatMulNet2, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=[1, 2], recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 32],), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out,))
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return out[0]
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net = MatMulNet2(_w1)
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compile_net(net)
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def test_NeighborExchange_empty_send_success():
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"""
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Feature: NeighborExchange
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Description: empty inputs, with valid arguments
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Expectation: success
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[], recv_rank_ids=[1], recv_shapes=([1],),
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send_shapes=(), recv_type=ms.float32)
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def construct(self, x1):
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self.alltoallv()
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return x1
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net = Net()
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_empty_recv_success():
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"""
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Feature: NeighborExchange
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Description: empty outputs, with valid arguments
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Expectation: success
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=[], recv_shapes=(),
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send_shapes=([32, 16],), recv_type=ms.float32)
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def construct(self, x1):
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self.alltoallv((x1,))
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return x1
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net = Net()
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_empty_send_empty_recv_success():
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"""
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Feature: NeighborExchange
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Description: empty inputs and empty outputs, with valid arguments
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Expectation: success
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[], recv_rank_ids=[], recv_shapes=(),
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send_shapes=(), recv_type=ms.float32)
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def construct(self, x1):
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self.alltoallv()
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return x1
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net = Net()
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_recv_shape_num_diff_with_recv_rank_size_failed():
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"""
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Feature: NeighborExchange
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Description: send_rank_ids and send_shapes are set as 1 input, but gives 2
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Expectation: throw ValueError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self, weight1):
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super(Net, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=[1, 2], recv_shapes=([32, 32],),
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send_shapes=([32, 32],), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out,))
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return out[0]
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net = Net(_w1)
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with pytest.raises(ValueError):
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compile_net(net)
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def test_NeighborExchange_send_shape_num_diff_with_send_rank_size_failed():
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"""
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Feature: NeighborExchange
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Description: send_rank_ids is set as 2 inputs, but send_shapes are set as 1 input
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Expectation: throw ValueError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self, weight1):
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super(Net, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0, 1], recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 32]),
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send_shapes=([32, 32],), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out,))
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return out[0]
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net = Net(_w1)
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with pytest.raises(ValueError):
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compile_net(net)
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def test_NeighborExchange_send_shape_num_diff_with_input_num_failed():
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"""
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Feature: NeighborExchange
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Description: send_rank_ids and send_shapes are set as 2 inputs, but has only 1 input
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Expectation: throw Exception
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self, weight1):
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super(Net, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0, 1], recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 32]),
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send_shapes=([32, 32], [32, 32]), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out,))
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return out[0]
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net = Net(_w1)
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with pytest.raises(Exception):
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compile_net(net)
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def test_NeighborExchange_send_shape_diff_with_input_shape_failed():
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"""
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Feature: NeighborExchange
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Description: send_shapes is set as [16, 16], but input is [32, 32]
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Expectation: throw Exception
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self, weight1):
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super(Net, self).__init__()
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self.matmul = P.MatMul()
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self.mul = P.Mul()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=[1, 2], recv_shapes=([32, 32], [32, 64]),
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send_shapes=([16, 16],), recv_type=ms.float32)
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self.weight1 = Parameter(weight1, "w1")
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def construct(self, x1, x2):
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out = self.matmul(x1, x2)
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out = self.mul(out, self.weight1)
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out = self.alltoallv((out,))
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return out[0]
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net = Net(_w1)
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with pytest.raises(Exception):
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compile_net(net)
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def test_NeighborExchange_attr_check_send_rank_ids_is_tuple_failed():
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"""
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Feature: NeighborExchange
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Description: send_rank_ids should be list, but a tuple is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=(0), recv_rank_ids=[1, 2], recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16],), recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_send_rank_ids_is_tuple_2_failed():
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"""
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Feature: NeighborExchange
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Description: send_rank_ids should be list, but a tuple is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=(0,), recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16],), recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_send_rank_ids_is_float_failed():
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"""
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Feature: NeighborExchange
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Description: send_rank_ids should be int, but a float is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[1.0], recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16],), recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_recv_rank_ids_is_tuple_failed():
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"""
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Feature: NeighborExchange
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Description: recv_rank_ids should be list, but a tuple is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=([1, 2],),
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16],), recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_recv_rank_ids_is_tuple_2_failed():
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"""
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Feature: NeighborExchange
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Description: recv_rank_ids should be list, but a tuple is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[0], recv_rank_ids=(1, 2,),
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16],), recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_recv_rank_ids_is_float_failed():
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"""
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Feature: NeighborExchange
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Description: recv_rank_ids should be int, but a float is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[1], recv_rank_ids=[1, 2.0],
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16],), recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_send_shape_not_tuple_failed():
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"""
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Feature: NeighborExchange
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Description: send_shapes should be tuple(list), but a list is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[1], recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16]), recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_send_shape_list_failed():
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"""
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Feature: NeighborExchange
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Description: send_shapes should be tuple(list), but a list(list) is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[1], recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=[[32, 16]], recv_type=ms.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
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net = Net()
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with pytest.raises(TypeError):
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_cell_graph_executor.compile(net, _x1)
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def test_NeighborExchange_attr_check_recv_type_numpy_failed():
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"""
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Feature: NeighborExchange
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Description: recv_type should be mindspore type, but a numpy type is given
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Expectation: throw TypeError
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"""
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.alltoallv = NeighborExchange(send_rank_ids=[1], recv_rank_ids=[1, 2],
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recv_shapes=([32, 32], [32, 64]),
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send_shapes=([32, 16],), recv_type=np.float32)
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def construct(self, x1):
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out = self.alltoallv((x1,))
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return out[0]
|
|
|
|
net = Net()
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|
with pytest.raises(TypeError):
|
|
_cell_graph_executor.compile(net, _x1)
|
|
|
|
|
|
def test_NeighborExchange_attr_invalid_grpup_failed():
|
|
"""
|
|
Feature: NeighborExchange
|
|
Description: group should be str, but a tuple is given
|
|
Expectation: throw TypeError
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
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|
self.alltoallv = NeighborExchange(send_rank_ids=[1], recv_rank_ids=[1, 2],
|
|
recv_shapes=([32, 32], [32, 64]),
|
|
send_shapes=([32, 16],), recv_type=ms.float32, group=("str",))
|
|
|
|
def construct(self, x1):
|
|
out = self.alltoallv((x1,))
|
|
return out[0]
|
|
|
|
net = Net()
|
|
with pytest.raises(TypeError):
|
|
_cell_graph_executor.compile(net, _x1)
|