forked from mindspore-Ecosystem/mindspore
402 lines
13 KiB
Python
402 lines
13 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
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from mindspore import context
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from mindspore.common.api import _cell_graph_executor
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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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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, b):
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predict = self.network(x, y, b)
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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, b):
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return grad_all(self.network)(x, y, b)
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def compile_net(net, x, y, b):
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, x, y, b)
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def test_matmul_equal():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.equal = P.Equal().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.equal(out, b)
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return out
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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, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_not_equal():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.notequal = P.NotEqual().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.notequal(out, b)
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return out
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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, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_approximateEqual():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.approximateEqual = P.ApproximateEqual(tolerance=0.5).shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.approximateEqual(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_greater():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.greater = P.Greater().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.greater(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_greaterEqual():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.greaterEqual = P.GreaterEqual().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.greaterEqual(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_less():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.less = P.Less().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.less(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_lessEqual():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.lessEqual = P.LessEqual().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.lessEqual(out, b)
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return out
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_not_equal_repeated_calculation():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.notequal = P.NotEqual().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.notequal(out, b)
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return out
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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, 2), (2, 2))
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strategy2 = ((4, 1), (4, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_maximum():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.maximum = P.Maximum().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.maximum(out, b)
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return out
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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, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_maximum_broadcast():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.maximum = P.Maximum().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.maximum(out, b)
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return out
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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, 2), (2, 2))
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strategy2 = ((4, 2), (2,))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_maximum_broadcast2():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.maximum = P.Maximum().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.maximum(out, b)
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return out
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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), (4, 1))
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strategy2 = ((4, 1), (1, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 1]), dtype=ms.float32)
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b = Tensor(np.ones([1, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_minimum():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.minimum = P.Minimum().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.minimum(out, b)
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return out
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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, 2), (2, 2))
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strategy2 = ((4, 2), (4, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_minimum_broadcast():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.minimum = P.Maximum().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.minimum(out, b)
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return out
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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, 2), (2, 2))
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strategy2 = ((4, 2), (2,))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_minimum_broadcast2():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy1)
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self.minimum = P.Minimum().shard(strategy2)
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.minimum(out, b)
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return out
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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), (4, 1))
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strategy2 = ((4, 1), (1, 2))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 1]), dtype=ms.float32)
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b = Tensor(np.ones([1, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_matmul_minimum_auto_parallel():
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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.matmul = P.MatMul()
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self.minimum = P.Minimum()
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def construct(self, x, y, b):
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out = self.matmul(x, y)
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out = self.minimum(out, b)
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return out
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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()))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 1]), dtype=ms.float32)
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b = Tensor(np.ones([1, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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