forked from mindspore-Ecosystem/mindspore
126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
# 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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# ============================================================================
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""" test sparse feature bprop """
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common.parameter import Parameter
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from mindspore.common.tensor import Tensor
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from mindspore.ops import composite as C, operations as P
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from mindspore.ops.operations.comm_ops import AllReduce
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell, Adam
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grad_all = C.GradOperation(get_all=True)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x):
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return grad_all(self.network)(x)
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def test_bprop_with_sparse_feature_allreduce():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="hybrid_parallel")
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context.set_context(enable_sparse=True)
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class Net(nn.Cell):
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def __init__(self, axis=0, shape=None):
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super(Net, self).__init__()
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if shape is None:
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shape = [8, 8]
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self.all_reduce = AllReduce()
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self.gatherv2 = P.SparseGatherV2()
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self.index = Tensor(np.ones(shape), dtype=ms.int32)
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self.axis = axis
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def construct(self, x):
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out = self.all_reduce(x)
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out = self.gatherv2(out, self.index, self.axis)
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return out
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net = GradWrap(Net())
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x = Tensor(np.ones([64, 64]), dtype=ms.float32)
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_executor.compile(net, x)
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def test_bprop_with_sparse_feature_mirror():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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context.set_context(enable_sparse=True)
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class Net(nn.Cell):
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def __init__(self, shape=None):
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super(Net, self).__init__()
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if shape is None:
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shape = [8, 8]
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self.index = Tensor(np.ones(shape), dtype=ms.int32)
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self.embeddinglookup = nn.EmbeddingLookup(64, 64, param_init='ones')
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self.embeddinglookup.embeddinglookup.set_strategy(((1, 1), (8, 1)))
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def construct(self, x, b):
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out = self.embeddinglookup(self.index)
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return out
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_x = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
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_b = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
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def compile_net(net):
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optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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_executor.compile(train_net, _x, _b)
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net = Net()
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compile_net(net)
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def test_bprop_with_sparse_feature_dataparallel():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="data_parallel")
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context.set_context(enable_sparse=True)
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class Net(nn.Cell):
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def __init__(self, axis=0, shape=None):
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super(Net, self).__init__()
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if shape is None:
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shape = [8, 8]
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weight = Tensor(np.ones([64, 64]), dtype=ms.float32)
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self.weight = Parameter(weight, "w")
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self.index = Tensor(np.ones(shape), dtype=ms.int32)
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self.axis = axis
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self.gatherv2 = P.SparseGatherV2()
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def construct(self, x, b):
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out = self.gatherv2(self.weight, self.index, self.axis)
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return out
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_x = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
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_b = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
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def compile_net(net):
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optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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_executor.compile(train_net, _x, _b)
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net = Net()
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compile_net(net)
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