forked from huawei/mindspore2022
1081 lines
38 KiB
Python
1081 lines
38 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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'''Reduce method ut'''
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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 NetWithLossNoBias(nn.Cell):
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def __init__(self, network):
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super(NetWithLossNoBias, 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 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 GradWrapNoBias(nn.Cell):
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def __init__(self, network):
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super(GradWrapNoBias, 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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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_no_bias(net, x, y):
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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 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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# model_parallel test
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def test_sum_mul():
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"""
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Feature: test ReduceSum model parallel strategy
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Description: partition the non-reduced axes, keep_dims is False
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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out = self.reduce_sum(out, (1,))
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out = self.mul2(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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strategy1 = ((1, 1, 8), (1, 1, 8))
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strategy2 = ((4, 1, 2),)
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strategy3 = ((2, 4), (2, 4))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 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_sum_mul2():
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"""
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Feature: test ReduceSum model parallel strategy
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Description: partition the reduced axes, keep_dims is False
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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out = self.reduce_sum(out, (0, 1))
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out = self.mul2(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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strategy1 = ((1, 1, 4, 2), (1, 1, 4, 2))
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strategy2 = ((2, 4, 1, 1),)
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strategy3 = ((2, 4), (2, 4))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 128, 64, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 128, 64, 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_sum_mul3():
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"""
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Feature: test ReduceSum model parallel strategy
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Description: partition the non-reduced axes, keep_dims is False
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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out = self.reduce_sum(out, -1)
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out = self.mul2(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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strategy1 = ((1, 4, 2), (1, 4, 2))
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strategy2 = ((4, 2, 1),)
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strategy3 = ((2, 4), (2, 4))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 32]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_sum_mul4():
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"""
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Feature: test ReduceSum model parallel strategy
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Description: partition the reduced axes, keep_dims is True
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_sum = P.ReduceSum(keep_dims=True).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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out = self.reduce_sum(out, -1)
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out = self.mul2(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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strategy1 = ((1, 4, 2), (1, 4, 2))
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strategy2 = ((2, 2, 2),)
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strategy3 = ((4, 2, 1), (4, 2, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 32, 1]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_sum_mul5():
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"""
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Feature: test ReduceSum model parallel strategy
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Description: partition the reduced axes, keep_dims is True
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_sum = P.ReduceSum(keep_dims=True).shard(strategy2)
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def construct(self, x, y):
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out = self.mul1(x, y)
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out = self.reduce_sum(out, 0)
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return out
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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strategy1 = ((1, 8, 8), (1, 8, 8))
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strategy2 = ((2, 4, 1),)
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net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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compile_net_no_bias(net, x, y)
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def test_sum_mul6():
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"""
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Feature: test ReduceSum model parallel strategy
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Description: partition the non-reduced axes, keep_dims is True
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_sum = P.ReduceSum(keep_dims=True).shard(strategy2)
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def construct(self, x, y):
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out = self.mul1(x, y)
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out = self.reduce_sum(out, 1)
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return out
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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strategy1 = ((1, 8, 8), (1, 8, 8))
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strategy2 = ((2, 1, 4),)
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net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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compile_net_no_bias(net, x, y)
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def test_sum_mul7():
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"""
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Feature: test ReduceSum model parallel strategy
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Description: partition the reduced axes, keep_dims is True
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_sum = P.ReduceSum(keep_dims=True).shard(strategy2)
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def construct(self, x, y):
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out = self.mul1(x, y)
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out = self.reduce_sum(out, (0, 1))
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return out
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context.set_auto_parallel_context(device_num=64, global_rank=0)
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strategy1 = ((1, 8, 8), (1, 8, 8))
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strategy2 = ((2, 4, 1),)
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net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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compile_net_no_bias(net, x, y)
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def test_max_mul():
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"""
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Feature: test ReduceMax model parallel strategy
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Description: partition the reduced axes, keep_dims is False
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_max = P.ReduceMax(keep_dims=False).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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out = self.reduce_max(out, -1)
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out = self.mul2(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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strategy1 = ((1, 4, 2), (1, 4, 2))
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strategy2 = ((4, 1, 2),)
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strategy3 = ((2, 4), (2, 4))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([128, 32]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_min_mul():
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"""
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Feature: test ReduceMin model parallel strategy
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Description: partition the reduced axes, keep_dims is False
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_min = P.ReduceMin(keep_dims=False).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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out = self.reduce_min(out, 0)
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out = self.mul2(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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strategy1 = ((1, 4, 2), (1, 4, 2))
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strategy2 = ((4, 1, 2),)
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strategy3 = ((2, 4), (2, 4))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([32, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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def test_reduce_mean_mul_float32():
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"""
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Feature: test ReduceMean model parallel strategy
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Description: partition the reduced axes, keep_dims is False
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_mean = P.ReduceMean(keep_dims=False).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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out = self.reduce_mean(out, 0)
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out = self.mul2(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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strategy1 = ((1, 4, 2), (1, 4, 2))
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strategy2 = ((4, 1, 2),)
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strategy3 = ((2, 4), (2, 4))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([32, 64]), dtype=ms.float32)
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compile_net(net, x, y, b)
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class ArgMaxWithValueNet(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(ArgMaxWithValueNet, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.arg_max_with_value = P.ArgMaxWithValue(keep_dims=False, axis=-1).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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_, out = self.arg_max_with_value(out)
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out = self.mul2(out, b)
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return out
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class ArgMinWithValueNet(nn.Cell):
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def __init__(self, strategy1, strategy2, strategy3):
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super(ArgMinWithValueNet, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.arg_min_with_value = P.ArgMinWithValue(keep_dims=False, axis=-1).shard(strategy2)
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self.mul2 = P.Mul().shard(strategy3)
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def construct(self, x, y, b):
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out = self.mul1(x, y)
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_, out = self.arg_min_with_value(out)
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out = self.mul2(out, b)
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return out
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class ArgMaxNet(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super(ArgMaxNet, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.arg_max = P.Argmax(axis=-1).shard(strategy2)
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def construct(self, x, y):
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out = self.mul1(x, y)
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out = self.arg_max(out)
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return out
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class ArgMinNet(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super(ArgMinNet, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.arg_min = P.Argmin(axis=-1).shard(strategy2)
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def construct(self, x, y):
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out = self.mul1(x, y)
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out = self.arg_min(out)
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return out
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def gen_inputs_and_compile_net(net):
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x = Tensor(np.ones([128, 64, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 64, 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 gen_inputs_and_compile_net_no_bias(net):
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x = Tensor(np.ones([128, 64, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 64, 64]), dtype=ms.float32)
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compile_net_no_bias(net, x, y)
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|
|
def tobefixed_test_arg_max_with_value_mul_semi_axis_parallel():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 2),)
|
|
strategy3 = ((2, 4), (2, 4))
|
|
net = GradWrap(NetWithLoss(ArgMaxWithValueNet(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net(net)
|
|
|
|
|
|
def test_arg_max_with_value_mul_semi():
|
|
"""
|
|
Feature: test ArgMaxWithValue semi parallel strategy
|
|
Description: partition the reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 1),)
|
|
strategy3 = ((2, 4), (2, 4))
|
|
net = GradWrap(NetWithLoss(ArgMaxWithValueNet(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net(net)
|
|
|
|
|
|
def test_arg_max_with_value_mul_auto():
|
|
"""
|
|
Feature: test ArgMaxWithValue auto parallel strategy
|
|
Description: don't set the strategy, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = None
|
|
strategy2 = None
|
|
strategy3 = None
|
|
net = GradWrap(NetWithLoss(ArgMaxWithValueNet(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
gen_inputs_and_compile_net(net)
|
|
|
|
|
|
def test_arg_min_with_value_mul_semi_axis_parallel():
|
|
"""
|
|
Feature: test ArgMinWithValue semi parallel strategy
|
|
Description: partition the reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 2),)
|
|
strategy3 = ((2, 4), (2, 4))
|
|
net = GradWrap(NetWithLoss(ArgMinWithValueNet(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net(net)
|
|
|
|
|
|
def test_arg_min_with_value_mul_semi():
|
|
"""
|
|
Feature: test ArgMinWithValue model parallel strategy
|
|
Description: partition the non-reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 1),)
|
|
strategy3 = ((2, 4), (2, 4))
|
|
net = GradWrap(NetWithLoss(ArgMinWithValueNet(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net(net)
|
|
|
|
|
|
def test_arg_min_with_value_mul_auto():
|
|
"""
|
|
Feature: test ArgMinWithValue auto parallel strategy
|
|
Description: don't set the strategy, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = None
|
|
strategy2 = None
|
|
strategy3 = None
|
|
net = GradWrap(NetWithLoss(ArgMinWithValueNet(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
gen_inputs_and_compile_net(net)
|
|
|
|
|
|
def test_arg_max_semi_axis_parallel():
|
|
"""
|
|
Feature: test Argmax semi parallel strategy
|
|
Description: partition the reduced axes
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 2),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMaxNet(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_arg_max_mul_semi():
|
|
"""
|
|
Feature: test Argmax model parallel strategy
|
|
Description: partition the non-reduced axes
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 2, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMaxNet(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_arg_max_mul_auto():
|
|
"""
|
|
Feature: test Argmax auto parallel strategy
|
|
Description: don't set the strategy
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = None
|
|
strategy2 = None
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMaxNet(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_arg_min_semi_axis_parallel():
|
|
"""
|
|
Feature: test Argmin semi parallel strategy
|
|
Description: partition the reduced axes
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 2),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMinNet(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_arg_min_mul_semi():
|
|
"""
|
|
Feature: test Argmin model parallel strategy
|
|
Description: partition the non-reduced axes
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 2, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMinNet(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_arg_min_mul_auto():
|
|
"""
|
|
Feature: test Argmin auto parallel strategy
|
|
Description: don't set the strategy
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = None
|
|
strategy2 = None
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMinNet(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
class ArgMinWithValueNet2(nn.Cell):
|
|
def __init__(self, strategy1, strategy2, strategy3):
|
|
super(ArgMinWithValueNet2, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.arg_min_with_value = P.ArgMinWithValue(keep_dims=True, axis=-1).shard(strategy2)
|
|
self.relu = P.ReLU().shard(strategy3)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
_, out = self.arg_min_with_value(out)
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
|
|
def tobefixed_test_arg_min_with_value_mul_semi_axis_parallel2():
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 2),)
|
|
strategy3 = ((2, 4, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMinWithValueNet2(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_arg_min_with_value_mul_semi2():
|
|
"""
|
|
Feature: test ArgMinWithValue semi parallel strategy
|
|
Description: partition the non-reduced axes, keep_dims is True
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 1),)
|
|
strategy3 = ((2, 4, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMinWithValueNet2(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_arg_min_with_value_mul_auto2():
|
|
"""
|
|
Feature: test ArgMinWithValue auto parallel strategy
|
|
Description: don't set the strategy, keep_dims is True
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = None
|
|
strategy2 = None
|
|
strategy3 = None
|
|
net = GradWrapNoBias(NetWithLossNoBias(ArgMinWithValueNet2(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_cross_batch():
|
|
"""
|
|
Feature: test ReduceMean semi parallel strategy with cross_batch
|
|
Description: partition the reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2, strategy3):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2)
|
|
self.reduce_mean = P.ReduceMean(keep_dims=False).shard(strategy3) \
|
|
.add_prim_attr("cross_batch", True)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.reduce_sum(out, -1)
|
|
out = self.reduce_mean(out, 0)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((4, 2), (4, 2))
|
|
strategy2 = ((2, 1),)
|
|
strategy3 = ((8,),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
|
|
def test_cross_batch2():
|
|
"""
|
|
Feature: test ReduceSum semi parallel strategy with cross_batch
|
|
Description: partition the reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2, strategy3):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.reduce_mean = P.ReduceMean(keep_dims=False).shard(strategy2)
|
|
self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy3) \
|
|
.add_prim_attr("cross_batch", True)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.reduce_mean(out, -1)
|
|
out = self.reduce_sum(out, 0)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((4, 2), (4, 2))
|
|
strategy2 = ((2, 1),)
|
|
strategy3 = ((8,),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
|
|
def test_cross_batch_auto():
|
|
"""
|
|
Feature: test ReduceSum auto parallel strategy with cross_batch
|
|
Description: don't set the strategy, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul()
|
|
self.reduce_mean = P.ReduceMean(keep_dims=False)
|
|
self.reduce_sum = P.ReduceSum(keep_dims=False).add_prim_attr("cross_batch", True)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.reduce_mean(out, -1)
|
|
out = self.reduce_sum(out, 0)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net()))
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
|
|
x = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
|
|
def test_max_empty_tuple():
|
|
"""
|
|
Feature: test ReduceMax semi parallel strategy
|
|
Description: partition the reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2, strategy3):
|
|
super(Net, self).__init__()
|
|
self.mul = P.Mul().shard(strategy1)
|
|
self.reduce_max = P.ReduceMax(keep_dims=False).shard(strategy2)
|
|
self.add = P.Add().shard(strategy3)
|
|
|
|
def construct(self, x, y, b):
|
|
out = self.mul(x, y)
|
|
out = self.reduce_max(out)
|
|
out = self.add(out, b)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 4, 2), (1, 4, 2))
|
|
strategy2 = ((4, 1, 2),)
|
|
strategy3 = ((), (1, 1))
|
|
net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
b = Tensor(np.ones([128, 32]), dtype=ms.float32)
|
|
|
|
compile_net(net, x, y, b)
|
|
|
|
|
|
def test_any_mul():
|
|
"""
|
|
Feature: test ReduceAny semi parallel strategy
|
|
Description: partition the reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.reduce_any = P.ReduceAny(keep_dims=False).shard(strategy2)
|
|
self.cast = P.Cast()
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.cast(out, ms.bool_)
|
|
out = self.reduce_any(out, 1)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=64, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 8, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
|
|
def test_any_mul2():
|
|
"""
|
|
Feature: test ReduceAny semi parallel strategy
|
|
Description: partition the non-reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.reduce_any = P.ReduceAny(keep_dims=False).shard(strategy2)
|
|
self.cast = P.Cast()
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.cast(out, ms.bool_)
|
|
out = self.reduce_any(out, -1)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=64, global_rank=0)
|
|
strategy1 = ((8, 1, 1), (8, 1, 1))
|
|
strategy2 = ((8, 1, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
def test_all_mul():
|
|
"""
|
|
Feature: test ReduceAll semi parallel strategy
|
|
Description: partition the reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.reduce_all = P.ReduceAll(keep_dims=False).shard(strategy2)
|
|
self.cast = P.Cast()
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.cast(out, ms.bool_)
|
|
out = self.reduce_all(out, 1)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((1, 8, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
|
|
def test_all_mul2():
|
|
"""
|
|
Feature: test ReduceAll semi parallel strategy
|
|
Description: partition the non-reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_all = P.ReduceAll(keep_dims=False).shard(strategy2)
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self.cast = P.Cast()
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def construct(self, x, y):
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out = self.mul1(x, y)
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out = self.cast(out, ms.bool_)
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out = self.reduce_all(out, -1)
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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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strategy1 = ((8, 1, 1), (8, 1, 1))
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strategy2 = ((8, 1, 1),)
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net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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compile_net_no_bias(net, x, y)
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def test_prod_mul():
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"""
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Feature: test ReduceProd model parallel strategy
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Description: partition the reduced axes, keep_dims is False
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Expectation: compile success
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"""
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super(Net, self).__init__()
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self.mul1 = P.Mul().shard(strategy1)
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self.reduce_prod = P.ReduceProd(keep_dims=False).shard(strategy2)
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|
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def construct(self, x, y):
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out = self.mul1(x, y)
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out = self.reduce_prod(out, 0)
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return out
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|
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context.set_auto_parallel_context(device_num=8, global_rank=0)
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strategy1 = ((1, 1, 8), (1, 1, 8))
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strategy2 = ((2, 4, 1),)
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net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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|
|
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x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
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compile_net_no_bias(net, x, y)
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|
|
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def test_prod_mul2():
|
|
"""
|
|
Feature: test ReduceProd model parallel strategy
|
|
Description: partition the non-reduced axes, keep_dims is False
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.reduce_prod = P.ReduceProd(keep_dims=False).shard(strategy2)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
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|
out = self.reduce_prod(out, -1)
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|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 8, 1), (1, 8, 1))
|
|
strategy2 = ((2, 4, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
def test_prod_mul3():
|
|
"""
|
|
Feature: test ReduceProd model parallel strategy
|
|
Description: partition the reduced axes, keep_dims is True
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, stra_mul, stra_prod):
|
|
super(Net, self).__init__()
|
|
self.mul = P.Mul().shard(stra_mul)
|
|
self.reduce_prod = P.ReduceProd(keep_dims=True).shard(stra_prod)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul(x, y)
|
|
out = self.reduce_prod(out, 0)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 1, 8), (1, 1, 8))
|
|
strategy2 = ((8, 1, 1),)
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
def test_prod_mul_auto():
|
|
"""
|
|
Feature: test ReduceProd auto parallel strategy
|
|
Description: don't set the strategy, keep_dims is True
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.reduce_prod = P.ReduceProd(keep_dims=True).shard(strategy2)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.reduce_prod(out, 0)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = None
|
|
strategy2 = None
|
|
net = GradWrapNoBias(NetWithLossNoBias(Net(strategy1, strategy2)))
|
|
context.set_auto_parallel_context(parallel_mode="auto_parallel")
|
|
gen_inputs_and_compile_net_no_bias(net)
|
|
|
|
|
|
def test_square_sum_all_mul():
|
|
"""
|
|
Feature: test SquareSumAll model parallel strategy
|
|
Description: partition the reduced axes
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, strategy1, strategy2):
|
|
super(Net, self).__init__()
|
|
self.mul1 = P.Mul().shard(strategy1)
|
|
self.square_sum_all = P.SquareSumAll().shard(strategy2)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul1(x, y)
|
|
out = self.square_sum_all(out, out)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 1, 8), (1, 1, 8))
|
|
strategy2 = ((2, 4, 1), (2, 4, 1))
|
|
net = Net(strategy1, strategy2)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|
|
|
|
|
|
def test_square_sum_all_mul2():
|
|
"""
|
|
Feature: test SquareSumAll model parallel strategy
|
|
Description: partition the reduced axes
|
|
Expectation: compile success
|
|
"""
|
|
class Net(nn.Cell):
|
|
def __init__(self, stra_mul, stra_prod):
|
|
super(Net, self).__init__()
|
|
self.mul = P.Mul().shard(stra_mul)
|
|
self.square_sum_all = P.SquareSumAll().shard(stra_prod)
|
|
|
|
def construct(self, x, y):
|
|
out = self.mul(x, y)
|
|
out = self.square_sum_all(out, out)
|
|
return out
|
|
|
|
context.set_auto_parallel_context(device_num=8, global_rank=0)
|
|
strategy1 = ((1, 1, 8), (1, 1, 8))
|
|
strategy2 = ((8, 1, 1), (8, 1, 1))
|
|
net = Net(strategy1, strategy2)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
x = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
y = Tensor(np.ones([128, 32, 64]), dtype=ms.float32)
|
|
compile_net_no_bias(net, x, y)
|