forked from mindspore-Ecosystem/mindspore
173 lines
6.3 KiB
Python
173 lines
6.3 KiB
Python
# Copyright 2019 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 numpy as np
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import mindspore as ms
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from mindspore import Tensor
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from mindspore import context
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.common import dtype as mstype
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from mindspore.common.api import _executor
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y):
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predict = self.network(x, y)
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return self.loss(predict)
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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, y):
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return C.grad_all(self.network)(x, y)
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class Net(nn.Cell):
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def __init__(self, axis=0, strategy1=None, strategy2=None, shape=[64, 64]):
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super().__init__()
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self.gatherv2 = P.GatherV2().set_strategy(strategy1)
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self.mul = P.Mul().set_strategy(strategy2)
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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, y):
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out = self.gatherv2(x, self.index, self.axis)
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out = self.mul(out, y)
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return out
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def test_gatherv2_semi_auto0():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy1 = ((1, 8), )
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto1():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy1 = ((8, 1), )
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto2():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 4), )
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto3():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy1 = ((1, 8), )
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto4():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy1 = ((8, 1), )
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto5():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 4), )
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto6():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(0, None, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto7():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy2 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(1, None, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_semi_auto8():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="semi_auto_parallel")
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strategy1 = ((8, ), )
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_auto0():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel")
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net = GradWrap(NetWithLoss(Net(0)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
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_executor.compile(net, x, y)
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def test_gatherv2_auto1():
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context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="auto_parallel")
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net = GradWrap(NetWithLoss(Net(1)))
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net.set_auto_parallel()
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
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_executor.compile(net, x, y)
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