forked from huawei/mindspore2022
272 lines
10 KiB
Python
272 lines
10 KiB
Python
# Copyright 2021 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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'''ResizeBilinear and ResizeNearestNeigbor ut'''
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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.common.api import _cell_graph_executor
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from mindspore.nn import Cell, TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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class Net(Cell):
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'''
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create the test Net
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'''
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def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
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strategy1=None, strategy2=None):
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super(Net, self).__init__()
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self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
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pad_mode=pad_mode, stride=stride).shard(strategy1)
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self.conv2d_weight = Parameter(conv2d_weight, "w1")
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self.resize_bilinear = P.ResizeBilinear((16, 16)).shard(strategy2)
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def construct(self, x):
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out = self.conv2d(x, self.conv2d_weight)
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out = self.resize_bilinear(out)
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return out
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class Net2(Cell):
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'''
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create the test Net
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'''
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def __init__(self, conv2d_weight, mul_weight, out_channel, kernel_size, pad_mode, stride, align_corners=False,
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strategy1=None, strategy2=None, out_strategy=None):
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super(Net2, self).__init__()
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self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
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pad_mode=pad_mode, stride=stride).shard(strategy1)
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self.conv2d_weight = Parameter(conv2d_weight, "w1")
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self.resize_neighbor = P.ResizeNearestNeighbor((16, 16), align_corners).shard(strategy2, out_strategy)
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self.mul = P.Mul()
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self.mul_weight = Parameter(mul_weight, "w2")
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def construct(self, x):
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out = self.conv2d(x, self.conv2d_weight)
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out = self.resize_neighbor(out)
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out = self.mul(out, self.mul_weight)
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return out
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class Net3(Cell):
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'''
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create the test Net
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'''
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def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
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strategy1=None):
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super(Net3, self).__init__()
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self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
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pad_mode=pad_mode, stride=stride).shard(strategy1)
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self.conv2d_weight = Parameter(conv2d_weight, "w1")
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self.resize_bilinear = P.ResizeBilinear((16, 16))
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def construct(self, x):
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out = self.conv2d(x, self.conv2d_weight)
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out = self.resize_bilinear(out)
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return out
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_x = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
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_w1 = Tensor(np.ones([8, 16, 2, 2]), dtype=ms.float32)
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_w2 = Tensor(np.ones([32, 8, 16, 16]), dtype=ms.float32)
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def compile_net(net, inputs=_x):
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_cell_graph_executor.compile(train_net, inputs)
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context.reset_auto_parallel_context()
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def test_bililear_data_parallel():
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"""
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Feature: test ResizeBilinear data parallel strategy
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Description: only shard batch 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))
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strategy2 = ((8, 1, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_bilinear_model_parallel1():
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"""
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Feature: test ResizeBilinear model parallel strategy
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Description: shard N/C
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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 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((4, 2, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_bilinear_repeated_calc():
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"""
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Feature: test ResizeBilinear repeated calculation parallel strategy
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Description: only shard batch dimension, but shard num smaller than device num
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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 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((2, 1, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_bilinear_auto_parallel():
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"""
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Feature: test ResizeBilinear auto parallel
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Description:
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
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compile_net(net)
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def test_bilinear_no_strategy():
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"""
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Feature: test ResizeBilinear semi auto parallel, and has not set strategy for it
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Description:
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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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net = Net3(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
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compile_net(net)
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def test_neighbor_data_parallel():
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"""
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Feature: test ResizeNearestNeighbor data parallel strategy
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Description: only shard batch 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))
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strategy2 = ((8, 1, 1, 1),)
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net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_neighbor_model_parallel_align_corners_shard_HW():
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"""
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Feature: test ResizeNearestNeighbor model parallel strategy
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Description: the align_corners is True, and shard N/C/H/W
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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 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((2, 2, 2, 2),)
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net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1, align_corners=True,
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strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net)
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def test_neighbor_out_strategy():
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"""
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Feature: test ResizeNearestNeighbor to set output parallel strategy
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Description:
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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 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((2, 2, 2, 2),)
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out_strategy = ((2, 2, 2, 2),)
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net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2, out_strategy=out_strategy)
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with pytest.raises(RuntimeError):
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compile_net(net)
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def test_neighbor_model_parallel_align_corners_shard_NC():
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"""
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Feature: test ResizeNearestNeighbor model parallel strategy
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Description: the align_corners is True, and shard N/C
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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=16, global_rank=0)
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strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((4, 4, 1, 1),)
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net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1, align_corners=True,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_neighbor_model_parallel_align_corners_is_false():
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"""
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Feature: test ResizeNearestNeighbor model parallel strategy
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Description: the align_corners is False, and shard N/C/H/W
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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=16, global_rank=0)
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strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((2, 2, 2, 2),)
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net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_neighbor_auto_parallel():
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"""
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Feature: test ResizeNearestNeighbor auto parallel
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Description:
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
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compile_net(net)
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def test_bilinear_shard_n_c_w():
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"""
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Feature: test ResizeBilinear shard n/c/w
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Description: shard n/c/w
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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=3)
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strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((2, 2, 1, 2),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_resizebilinear_shard_W_in_GPU():
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"""
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Feature: test ResizeBilinear
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Description: the platform is GPU, and shard n/c/w
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Expectation: compile failed, can not shard h or w dimension in GPU
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"""
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context.set_context(device_target="GPU")
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=3)
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strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
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strategy2 = ((2, 2, 1, 2),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
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strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net)
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