forked from mindspore-Ecosystem/mindspore
269 lines
11 KiB
Python
269 lines
11 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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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 Tensor, Parameter
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from mindspore import context
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from mindspore.common.api import _executor
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from mindspore.context import set_auto_parallel_context, reset_auto_parallel_context
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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# model_parallel test
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def test_six_matmul_save():
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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, x1, x6):
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predict = self.network(x1, x6)
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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, x1, x6):
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return grad_all(self.network)(x1, x6)
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3, strategy4, strategy5, strategy6):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul2 = P.MatMul().set_strategy(strategy2)
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self.matmul3 = P.MatMul().set_strategy(strategy3)
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self.matmul4 = P.MatMul().set_strategy(strategy4)
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self.matmul5 = P.MatMul().set_strategy(strategy5)
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self.matmul6 = P.MatMul().set_strategy(strategy6)
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self.weight1 = Parameter(Tensor(np.ones([32, 64]), dtype=ms.float32), name="weight1")
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self.weight2 = Parameter(Tensor(np.ones([64, 64]), dtype=ms.float32), name="weight2")
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self.weight3 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight3")
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self.weight4 = Parameter(Tensor(np.ones([128, 64]), dtype=ms.float32), name="weight4")
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self.weight5 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight5")
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self.weight6 = Parameter(Tensor(np.ones([32, 128]), dtype=ms.float32), name="weight6")
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def construct(self, x1, x6):
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out = self.matmul1(x1, self.weight1)
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out = self.matmul2(out, self.weight2)
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out = self.matmul3(out, self.weight3)
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out = self.matmul4(out, self.weight4)
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out = self.matmul5(out, self.weight5)
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out = out + self.weight6
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out = self.matmul6(out, x6)
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return out
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reset_auto_parallel_context()
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set_auto_parallel_context(device_num=8, global_rank=0, strategy_ckpt_save_file="./strategy_stage1.ckpt")
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strategy1 = ((8, 1), (1, 1))
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strategy2 = ((1, 8), (8, 1))
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strategy3 = ((2, 2), (2, 2))
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strategy4 = ((1, 1), (1, 8))
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strategy5 = ((4, 2), (2, 1))
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strategy6 = ((4, 1), (1, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3, strategy4, strategy5, strategy6)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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x1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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x6 = Tensor(np.ones([128, 32]), dtype=ms.float32)
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_executor.compile(net, x1, x6)
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# remove matmul2, add matmul7
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def test_six_matmul_load():
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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, x1, x6, x7):
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predict = self.network(x1, x6, x7)
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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, x1, x6, x7):
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return grad_all(self.network)(x1, x6, x7)
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy3, strategy4, strategy5, strategy6, strategy7):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul3 = P.MatMul().set_strategy(strategy3)
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self.matmul4 = P.MatMul().set_strategy(strategy4)
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self.matmul5 = P.MatMul().set_strategy(strategy5)
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self.matmul6 = P.MatMul().set_strategy(strategy6)
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self.matmul7 = P.MatMul().set_strategy(strategy7)
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self.weight1 = Parameter(Tensor(np.ones([32, 64]), dtype=ms.float32), name="weight1")
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self.weight3 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight3")
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self.weight4 = Parameter(Tensor(np.ones([128, 64]), dtype=ms.float32), name="weight4")
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self.weight5 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight5")
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self.weight6 = Parameter(Tensor(np.ones([32, 128]), dtype=ms.float32), name="weight6")
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def construct(self, x1, x6, x7):
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out = self.matmul1(x1, self.weight1)
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out = self.matmul3(out, self.weight3)
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out = self.matmul4(out, self.weight4)
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out = self.matmul5(out, self.weight5)
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out = out + self.weight6
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out = self.matmul6(out, x6)
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out = self.matmul7(out, x7)
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return out
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reset_auto_parallel_context()
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set_auto_parallel_context(device_num=8, global_rank=0, strategy_ckpt_load_file="./strategy_stage1.ckpt")
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strategy1 = ((8, 1), (1, 1))
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strategy3 = ((8, 1), (1, 1))
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strategy4 = ((8, 1), (1, 1))
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strategy5 = ((8, 1), (1, 1))
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strategy6 = ((8, 1), (1, 1))
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strategy7 = ((8, 1), (1, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy3, strategy4, strategy5, strategy6, strategy7)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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net.set_auto_parallel()
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x1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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x6 = Tensor(np.ones([128, 32]), dtype=ms.float32)
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x7 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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_executor.compile(net, x1, x6, x7)
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# model_parallel test
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def test_six_matmul_save_auto():
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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, x1, x6):
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predict = self.network(x1, x6)
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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, x1, x6):
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return grad_all(self.network)(x1, x6)
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.matmul1 = P.MatMul()
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self.matmul2 = P.MatMul()
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self.matmul3 = P.MatMul()
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self.matmul4 = P.MatMul()
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self.matmul5 = P.MatMul()
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self.matmul6 = P.MatMul()
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self.weight1 = Parameter(Tensor(np.ones([32, 64]), dtype=ms.float32), name="weight1")
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self.weight2 = Parameter(Tensor(np.ones([64, 64]), dtype=ms.float32), name="weight2")
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self.weight3 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight3")
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self.weight4 = Parameter(Tensor(np.ones([128, 64]), dtype=ms.float32), name="weight4")
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self.weight5 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight5")
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self.weight6 = Parameter(Tensor(np.ones([32, 128]), dtype=ms.float32), name="weight6")
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def construct(self, x1, x6):
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out = self.matmul1(x1, self.weight1)
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out = self.matmul2(out, self.weight2)
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out = self.matmul3(out, self.weight3)
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out = self.matmul4(out, self.weight4)
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out = self.matmul5(out, self.weight5)
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out = out + self.weight6
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out = self.matmul6(out, x6)
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return out
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reset_auto_parallel_context()
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set_auto_parallel_context(device_num=8, global_rank=0, strategy_ckpt_save_file="./strategy_stage1_auto.ckpt")
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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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net.set_auto_parallel()
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x1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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x6 = Tensor(np.ones([128, 32]), dtype=ms.float32)
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_executor.compile(net, x1, x6)
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# remove matmul2, add matmul7
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def test_six_matmul_load_auto():
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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, x1, x6, x7):
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predict = self.network(x1, x6, x7)
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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, x1, x6, x7):
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return grad_all(self.network)(x1, x6, x7)
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy3, strategy4, strategy5):
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super().__init__()
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self.matmul1 = P.MatMul().set_strategy(strategy1)
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self.matmul3 = P.MatMul().set_strategy(strategy3)
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self.matmul4 = P.MatMul().set_strategy(strategy4)
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self.matmul5 = P.MatMul().set_strategy(strategy5)
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self.matmul6 = P.MatMul()
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self.matmul7 = P.MatMul()
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self.weight1 = Parameter(Tensor(np.ones([32, 64]), dtype=ms.float32), name="weight1")
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self.weight3 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight3")
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self.weight4 = Parameter(Tensor(np.ones([128, 64]), dtype=ms.float32), name="weight4")
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self.weight5 = Parameter(Tensor(np.ones([64, 128]), dtype=ms.float32), name="weight5")
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self.weight6 = Parameter(Tensor(np.ones([32, 128]), dtype=ms.float32), name="weight6")
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def construct(self, x1, x6, x7):
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out = self.matmul1(x1, self.weight1)
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out = self.matmul3(out, self.weight3)
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out = self.matmul4(out, self.weight4)
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out = self.matmul5(out, self.weight5)
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out = out + self.weight6
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out = self.matmul6(out, x6)
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out = self.matmul7(out, x7)
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return out
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reset_auto_parallel_context()
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set_auto_parallel_context(device_num=8, global_rank=0, strategy_ckpt_load_file="./strategy_stage1_auto.ckpt")
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strategy1 = ((2, 2), (2, 2))
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strategy3 = ((2, 2), (2, 2))
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strategy4 = ((2, 2), (2, 2))
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strategy5 = ((2, 2), (2, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy3, strategy4, strategy5)))
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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net.set_auto_parallel()
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x1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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x6 = Tensor(np.ones([128, 32]), dtype=ms.float32)
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x7 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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_executor.compile(net, x1, x6, x7)
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