forked from mindspore-Ecosystem/mindspore
390 lines
13 KiB
Python
390 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 pytest
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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, context
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from mindspore.common import dtype as mstype
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from mindspore.common import Parameter
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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 mindspore.ops import functional as F
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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):
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predict = self.network(x)
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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):
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return grad_all(self.network)(x)
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class NetWithLossTwoInput(nn.Cell):
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def __init__(self, network):
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super(NetWithLossTwoInput, 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 GradWrapTwoInput(nn.Cell):
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def __init__(self, network):
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super(GradWrapTwoInput, self).__init__()
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self.network = network
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def construct(self, x, y):
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return grad_all(self.network)(x, y)
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def compile_graph(net, device_num, x):
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context.set_auto_parallel_context(device_num=device_num, global_rank=0, parallel_mode="auto_parallel",
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search_mode="sharding_propagation")
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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)
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def compile_graph_two_input(net, device_num, x, y):
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context.set_auto_parallel_context(device_num=device_num, global_rank=0, parallel_mode="auto_parallel",
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search_mode="sharding_propagation")
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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)
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def test_reshape_reshape():
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"""
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Feature: Sharding propagation for Reshape.
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Description: ReLU->Reshape
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Expectation: compile done without error.
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"""
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device_num = 8
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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.reshape = P.Reshape()
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self.relu = P.ReLU().shard(((1, 1, 1, 1),))
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def construct(self, x):
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x = self.relu(x)
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out = self.reshape(x, (64, 28))
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out = self.reshape(out, (64, 28, 1))
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return out
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x = Tensor(np.ones([device_num * 8, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, device_num, x)
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def test_reshape_auto_1():
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"""
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Feature: Sharding propagation for Reshape.
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Description: ReLU->Reshape->MatMul
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Expectation: compile done without error.
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"""
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device_num = 8
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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.relu = P.ReLU().shard(((1, 1, 1, 1),))
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().shard(((2, 1), (1, 4)))
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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def construct(self, x):
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x = self.relu(x)
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out = self.reshape(x, (64, 28))
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out = self.matmul(out, self.matmul_weight)
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return out
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x = Tensor(np.ones([device_num * 8, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, device_num, x)
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def test_reshape_auto_2():
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"""
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Feature: Sharding propagation for Reshape.
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Description: ReLU->Reshape->MatMul->Reshape->Add
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Expectation: compile done without error.
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"""
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device_num = 8
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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.relu = P.ReLU()
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self.relu2 = P.ReLU()
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().shard(((2, 1), (1, 4)))
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self.matmul_weight = Parameter(Tensor(np.ones([28, 64]), dtype=ms.float32), name="weight")
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self.add = P.Add().shard(((2, 4), (2, 4)))
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self.add_weight = Parameter(Tensor(np.ones([128, 32]), dtype=ms.float32), name="weight1")
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def construct(self, x):
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out = self.relu(x)
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out = self.relu2(out)
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out = self.reshape(out, (64, 28))
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out = self.matmul(out, self.matmul_weight)
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out = self.reshape(out, (128, 32))
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out = self.add(out, self.add_weight)
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return out
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x = Tensor(np.ones([device_num * 8, 28, 1, 1]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, device_num, x)
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def test_reshape_auto_3():
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"""
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Feature: Sharding propagation for Reshape.
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Description: Mul->Add->Cast->Reshape->Cast->ReduceMean
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Expectation: compile done without error.
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"""
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device_num = 8
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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.gamma = Parameter(Tensor(np.ones([1024]), dtype=ms.float32), name="gamma")
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self.beta = Parameter(Tensor(np.ones([1024]), dtype=ms.float32), name="beta")
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self.add = P.TensorAdd().shard(((8, 1, 1), (1,)))
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self.mul = P.Mul().shard(((8, 1, 1), (1,)))
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self.mean = P.ReduceMean(keep_dims=True).shard(((8, 1),))
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self.reshape = P.Reshape()
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self.dtype1 = mstype.float16
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self.dtype2 = mstype.float32
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def construct(self, x):
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out = self.add(self.mul(x, self.gamma), self.beta)
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out = F.cast(out, self.dtype1)
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out = self.reshape(out, (-1, 1024))
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out = F.cast(out, self.dtype2)
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out = self.mean(out, -1)
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return out
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x = Tensor(np.ones([2048, 30, 1024]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, device_num, x)
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def test_reshape_auto_4():
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"""
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Feature: Sharding propagation for Reshape.
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Description: Mul->Add->Cast->Reshape->Cast->ReduceMean
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Expectation: compile done without error.
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"""
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device_num = 8
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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.gamma = Parameter(Tensor(np.ones([1024]), dtype=ms.float32), name="gamma")
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self.beta = Parameter(Tensor(np.ones([1024]), dtype=ms.float32), name="beta")
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self.add = P.TensorAdd().shard(((8, 1, 1), (1,)))
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self.mul = P.Mul().shard(((8, 1, 1), (1,)))
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self.mean = P.ReduceMean(keep_dims=True)
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self.reshape = P.Reshape()
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self.dtype1 = mstype.float16
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self.dtype2 = mstype.float32
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def construct(self, x):
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out = self.add(self.mul(x, self.gamma), self.beta)
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out = F.cast(out, self.dtype1)
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out = self.reshape(out, (-1, 1024))
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out = F.cast(out, self.dtype2)
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out = self.mean(out, -1)
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return out
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x = Tensor(np.ones([2048, 30, 1024]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, device_num, x)
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def test_reshape_auto_5():
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"""
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Feature: Sharding propagation for Reshape.
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Description: Mul->Add->Cast->Reshape->Cast->ReduceMean
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Expectation: compile done without error.
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"""
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device_num = 8
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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.gamma = Parameter(Tensor(np.ones([1024]), dtype=ms.float32), name="gamma")
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self.beta = Parameter(Tensor(np.ones([1024]), dtype=ms.float32), name="beta")
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self.add = P.TensorAdd().shard(((8, 1, 1), (1,)))
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self.mul = P.Mul()
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self.mean = P.ReduceMean(keep_dims=True).shard(((2, 4),))
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self.reshape = P.Reshape()
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self.dtype1 = mstype.float16
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self.dtype2 = mstype.float32
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def construct(self, x):
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out = self.add(self.mul(x, self.gamma), self.beta)
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out = self.reshape(out, (-1, 1024))
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out = self.mean(out, -1)
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return out
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x = Tensor(np.ones([2048, 30, 1024]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, device_num, x)
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def test_reshape_auto_6():
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"""
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Feature: Sharding propagation for Reshape.
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Description: Reshape->ReLU->Mul->Reshape->Add->Mul->Reshape->Add
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Expectation: compile done without error.
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"""
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device_num = 8
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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.relu = P.ReLU()
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self.mul = P.Mul().shard(((8, 1, 1), (8, 1, 1)))
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self.reshape = P.Reshape()
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self.reduce_sum = P.ReduceSum()
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self.wide_w = Parameter(Tensor(np.ones([8, 1024*8, 64]), dtype=ms.float32), name="weight")
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def construct(self, x, y):
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mask = self.reshape(y, (8, 1024*8, 1))
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w_id = self.relu(x)
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wx = self.mul(w_id, mask)
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wide_out = self.reshape(self.reduce_sum(wx, 1), (-1, 1))
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deep_id = x + self.wide_w
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vx = self.mul(deep_id, mask)
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deep_in = self.reshape(vx, (-1, 1024*8*64))
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out = wide_out + deep_in
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return out
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x = Tensor(np.ones([8, 1024*device_num, 1]), dtype=ms.float32)
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y = Tensor(np.ones([8, 1024*device_num]), dtype=ms.float32)
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net = GradWrapTwoInput(NetWithLossTwoInput(Net()))
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compile_graph_two_input(net, device_num, x, y)
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def test_reshape_depend_reshape():
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"""
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Feature: Sharding propagation for Reshape.
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Description: Mul->ReLU->Reshape->Reshape->Add
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Expectation: compile with error.
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"""
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device_num = 8
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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.reshape1 = P.Reshape()
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self.reshape2 = P.Reshape()
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self.relu = P.ReLU()
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self.depend = P.Depend()
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self.mul = P.Mul().shard(((2, 4), (2, 4)))
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self.mul_weight = Parameter(Tensor(np.ones([128, 96]), dtype=ms.float32), name="weight")
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self.add = P.Add().shard(((4, 2), (4, 2)))
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def construct(self, x, y):
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out1 = self.mul(x, self.mul_weight)
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y = self.relu(y)
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out2 = self.reshape1(y, (96, 32, 4))
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out3 = self.depend(out2, out1)
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out3 = self.reshape2(out3, (128, 96))
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out = out1 + out3
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return out
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class NetWithLoss1(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss1, self).__init__()
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self.mean = P.ReduceMean(keep_dims=False)
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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.mean(predict, ())
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x = Tensor(np.ones([128, 96]), dtype=ms.float32)
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y = Tensor(np.ones([256, 48]), dtype=ms.float32)
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net = GradWrapTwoInput(NetWithLoss1(Net()))
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with pytest.raises(RuntimeError):
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compile_graph_two_input(net, device_num, x, y)
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def test_reshape_auto_8():
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"""
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Feature: Sharding propagation for common parameter being used by multiple ops.
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Description: relu->add->mul->mean
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Expectation: compile done without error.
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"""
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device_num = 8
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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.gamma = Parameter(Tensor(np.ones([2048, 2048]), dtype=ms.float32), name="gamma")
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self.add = P.TensorAdd()
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self.relu = P.ReLU().shard(((1, 1),))
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self.mul2 = P.MatMul().shard(((1, 1), (1, 8)))
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self.mean = P.ReduceMean(keep_dims=True)
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def construct(self, x):
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out = self.add(x, self.relu(self.gamma))
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out = self.mul2(out, self.gamma)
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out = self.mean(out, -1)
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return out
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x = Tensor(np.ones([2048, 2048]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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with pytest.raises(RuntimeError):
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compile_graph(net, device_num, x)
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def test_reshape_auto_9():
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"""
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Feature: Sharding propagation for common parameter being used by multiple ops.
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Description: relu->add->mul->mean
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Expectation: compile done without error.
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"""
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device_num = 8
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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.gamma = Parameter(Tensor(np.ones([2048, 2048]), dtype=ms.float32), name="gamma")
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self.add = P.TensorAdd()
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self.relu = P.ReLU().shard(((1, 1),))
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self.mul2 = P.MatMul().shard(((8, 1), (1, 1)))
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self.mean = P.ReduceMean(keep_dims=True)
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def construct(self, x):
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out = self.add(x, self.relu(self.gamma))
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out = self.mul2(out, self.gamma)
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out = self.mean(out, -1)
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return out
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x = Tensor(np.ones([2048, 2048]), dtype=ms.float32)
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net = GradWrap(NetWithLoss(Net()))
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compile_graph(net, device_num, x)
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