forked from huawei/mindspore2022
128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
# Copyright 2022 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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import numpy as np
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import mindspore.common.dtype as mstype
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from mindspore import Tensor, context
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from parallel.utils.utils import ParallelValidator, compile_net
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logits_ = Tensor(np.random.uniform(0, 1, [8, 8]), mstype.float32)
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labels_ = Tensor(np.random.randint(0, 10, [8, 8]), mstype.float32)
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class Net(Cell):
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def __init__(self, reduction, strategy=None):
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super(Net, self).__init__()
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self.kldiv_loss = P.KLDivLoss(reduction).shard(strategy)
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def construct(self, logits, labels):
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out = self.kldiv_loss(logits, labels)
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return out
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def test_kldiv_loss_mean_auto_parallel():
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"""
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Features: test KLDivLoss auto parallel
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Description: auto parallel, reduction is 'mean'
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0, full_batch=True)
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reduction = 'mean'
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net = Net(reduction)
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compile_net(net, logits_, labels_)
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def test_kldiv_loss_none_auto_parallel():
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"""
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Features: test KLDivLoss auto parallel
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Description: auto parallel, reduction is 'none'
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0, full_batch=True)
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reduction = 'none'
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net = Net(reduction)
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compile_net(net, logits_, labels_)
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def test_kldiv_loss_sum_auto_parallel():
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"""
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Features: test KLDivLoss auto parallel
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Description: auto parallel, reduction is 'sum'
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0, full_batch=True)
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reduction = 'sum'
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net = Net(reduction)
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compile_net(net, logits_, labels_)
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def test_kldiv_loss_mean_data_parallel():
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"""
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Features: test KLDivLoss data parallel
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Description: data parallel, reduction is 'mean'
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=1)
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reduction = 'mean'
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net = Net(reduction)
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phase = compile_net(net, logits_, labels_)
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validator = ParallelValidator(net, phase)
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assert validator.check_node_inputs('AllReduce-0', ['KLDivLoss-0'])
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assert validator.check_node_attrs('AllReduce-0', {'op': 'sum'})
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def test_kldiv_loss_none_data_parallel():
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"""
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Features: test KLDivLoss data parallel
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Description: data parallel, reduction is 'none'
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=1)
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reduction = 'none'
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net = Net(reduction)
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compile_net(net, logits_, labels_)
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def test_kldiv_loss_none_model_parallel():
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"""
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Features: test KLDivLoss model parallel
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Description: model parallel, reduction is 'none'
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=5)
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reduction = 'none'
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strategy = ((2, 2), (2, 2))
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net = Net(reduction, strategy)
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compile_net(net, logits_, labels_)
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def test_kldiv_loss_mean_model_parallel():
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"""
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Features: test KLDivLoss model parallel
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Description: model parallel, reduction is 'mean'
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=5)
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reduction = 'mean'
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strategy = ((4, 2), (4, 2))
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net = Net(reduction, strategy)
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phase = compile_net(net, logits_, labels_)
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validator = ParallelValidator(net, phase)
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assert validator.check_node_inputs('AllReduce-0', ['KLDivLoss-0'])
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assert validator.check_node_attrs('AllReduce-0', {'op': 'sum'})
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