forked from mindspore-Ecosystem/mindspore
147 lines
5.9 KiB
Python
147 lines
5.9 KiB
Python
# Copyright 2022 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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from mindspore import context, Tensor, Parameter
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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from parallel.utils.utils import ParallelValidator, compile_net
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def setup_function():
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context.set_auto_parallel_context(dataset_strategy="full_batch")
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class Net(Cell):
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def __init__(self, conv3d_weight, out_channel, kernel_size, pad_mode, stride, dilation=1, group=1, pad=0,
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strategy1=None, strategy2=None):
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super().__init__()
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self.conv3d = P.Conv3D(out_channel=out_channel, kernel_size=kernel_size, pad_mode=pad_mode, pad=pad,
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stride=stride, dilation=dilation, group=group).shard(strategy1)
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self.neg = P.Neg().shard(strategy2)
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self.conv3d_weight = Parameter(conv3d_weight, "w1")
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def construct(self, x, b):
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out = self.conv3d(x, self.conv3d_weight)
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out = self.neg(out)
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return out
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_x = Tensor(np.ones([32, 16, 8, 8, 8]), dtype=ms.float32)
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_x3 = Tensor(np.ones([32, 16, 16, 16, 16]), dtype=ms.float32)
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_x4 = Tensor(np.ones([2, 16, 56, 56, 24]), dtype=ms.float32)
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_w1 = Tensor(np.ones([8, 16, 2, 2, 2]), dtype=ms.float32)
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_w2 = Tensor(np.ones([8, 16, 3, 3, 3]), dtype=ms.float32)
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_w5 = Tensor(np.ones([8, 16, 4, 4, 4]), dtype=ms.float32)
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_b = Tensor(np.ones([32, 16, 8, 8, 8]), dtype=ms.float32)
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def test_conv3d_data_parallel():
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"""
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Feature: test conv3d data parallel
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Description: shard n dimension
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((8, 1, 1, 1, 1), (1, 1, 1, 1, 1))
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strategy2 = ((8, 1, 1, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
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phase = compile_net(net, _x, _b)
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validator = ParallelValidator(net, phase)
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assert validator.check_node_inputs('Neg-0', ['Conv3D-0'])
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def test_conv3d_pad_mode_overlap_is_negative():
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"""
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Feature: test conv3d pad mode and overlap is negative
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Description: shard d/h
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Expectation: compile failed
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((1, 1, 4, 4, 1), (1, 1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 1, 1),)
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net = Net(_w5, out_channel=8, kernel_size=4, pad_mode="pad", stride=5, pad=(3, 0, 3, 0, 3, 0),
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strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x3, _b)
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def test_conv3d_pad_mode_unet_3d_rank0():
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"""
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Feature: test pad mode unet 3d
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Description: shard d/h
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 1, 2, 4, 1), (1, 1, 1, 1, 1))
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strategy2 = ((1, 1, 2, 4, 1),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="pad", stride=2, pad=1,
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strategy1=strategy1, strategy2=strategy2)
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phase = compile_net(net, _x4, _b)
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validator = ParallelValidator(net, phase)
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assert validator.check_node_attrs('NeighborExchangeV2-0', {'send_lens': '[0, 1, 0, 1]'})
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assert validator.check_node_attrs('NeighborExchangeV2-0', {'recv_lens': '[0, 0, 0, 0]'})
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def test_conv3d_pad_mode_unet_3d_rank1():
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"""
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Feature: test pad mode unet 3d
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Description: shard d/h
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=1)
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strategy1 = ((1, 1, 2, 4, 1), (1, 1, 1, 1, 1))
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strategy2 = ((1, 1, 2, 4, 1),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="pad", stride=2, pad=1,
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strategy1=strategy1, strategy2=strategy2)
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phase = compile_net(net, _x4, _b)
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validator = ParallelValidator(net, phase)
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assert validator.check_node_attrs('NeighborExchangeV2-0', {'send_lens': '[0, 1, 0, 1]'})
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assert validator.check_node_attrs('NeighborExchangeV2-0', {'recv_lens': '[0, 0, 1, 0]'})
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def test_conv3d_pad_mode_unet_3d_rank7():
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"""
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Feature: test pad mode unet 3d
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Description: shard d/h
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=7)
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strategy1 = ((1, 1, 2, 4, 1), (1, 1, 1, 1, 1))
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strategy2 = ((1, 1, 2, 4, 1),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="pad", stride=2, pad=1,
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strategy1=strategy1, strategy2=strategy2)
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phase = compile_net(net, _x4, _b)
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validator = ParallelValidator(net, phase)
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assert validator.check_node_attrs('NeighborExchangeV2-0', {'send_lens': '[0, 0, 0, 0]'})
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assert validator.check_node_attrs('NeighborExchangeV2-0', {'recv_lens': '[1, 0, 1, 0]'})
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def test_conv3d_valid_mode_output_shape_cannot_div_by_strategy():
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"""
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Feature: test valid mode, and output shape can not div by strategy
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Description: shard d
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Expectation: compile failed
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 1, 1, 8, 1), (1, 1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="valid", stride=4,
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strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x3, _b)
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