forked from huawei/mindspore2022
194 lines
8.4 KiB
Python
194 lines
8.4 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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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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def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride, pool_kernel_size, pool_strides,
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strategy1=None, strategy2=None):
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super().__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.max_pool = P.MaxPool(kernel_size=pool_kernel_size, strides=pool_strides).shard(strategy2)
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def construct(self, x, b):
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out = self.conv2d(x, self.conv2d_weight)
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out = self.max_pool(out)
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return out
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class Net2(Cell):
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def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride, pool_kernel_size, pool_strides,
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strategy1=None, strategy2=None):
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super().__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.avg_pool = P.AvgPool(kernel_size=pool_kernel_size, strides=pool_strides).shard(strategy2)
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def construct(self, x, b):
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out = self.conv2d(x, self.conv2d_weight)
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out = self.avg_pool(out)
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return out
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_x0 = Tensor(np.ones([32, 16, 10, 10]), dtype=ms.float32)
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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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_b = Tensor(np.ones([32, 16, 8, 8]), 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, _b)
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context.reset_auto_parallel_context()
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def test_maxpool_data_parallel():
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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, pool_kernel_size=2, pool_strides=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_maxpool_model_parallel1():
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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, 2, 2),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_maxpool_model_parallel2():
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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, 2, 2),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=4,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_maxpool_auto_parallel():
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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, pool_kernel_size=2, pool_strides=4)
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compile_net(net)
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def test_maxpool_output_is_not_divisible_by_strategy_w_dimension():
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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 = ((1, 1, 1, 8),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
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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_maxpool_output_is_not_divisible_by_strategy_h_dimension():
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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 = ((1, 1, 8, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
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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_maxpool_shard_h_and_kernel_size_larger_than_stride():
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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 = ((1, 1, 2, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=3, pool_strides=2,
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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_maxpool_shard_w_and_kernel_size_larger_than_stride():
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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 = ((1, 1, 1, 2),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=3, pool_strides=2,
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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_maxpool_shard_h_and_input_slice_is_not_divisible_by_stride():
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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 = ((1, 1, 2, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=1, pool_strides=3,
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strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, inputs=_x0)
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def test_maxpool_shard_w_and_input_slice_is_not_divisible_by_stride():
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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 = ((1, 1, 2, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=1, pool_strides=3,
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strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, inputs=_x0)
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def test_avgpool_data_parallel():
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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, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_avgpool_model_parallel1():
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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, 2, 2),)
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net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_avgpool_model_parallel2():
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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, 2, 2),)
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net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=4,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_avgpool_auto_parallel():
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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, out_channel=8, kernel_size=2, pad_mode="same", stride=1, pool_kernel_size=2, pool_strides=4)
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compile_net(net)
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