forked from huawei/mindspore2022
286 lines
12 KiB
Python
286 lines
12 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, dilation=1, group=1,
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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, dilation=dilation, group=group).shard(strategy1)
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self.neg = P.Neg().shard(strategy2)
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self.conv2d_weight = Parameter(conv2d_weight, "w1")
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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.neg(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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_x2 = Tensor(np.ones([32, 16, 10, 10]), dtype=ms.float32)
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_w0 = Tensor(np.ones([8, 16, 1, 1]), 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([8, 16, 3, 3]), dtype=ms.float32)
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_w3 = Tensor(np.ones([8, 16, 5, 5]), dtype=ms.float32)
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_w4 = Tensor(np.ones([8, 8, 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, input_x=_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, input_x, _b)
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context.reset_auto_parallel_context()
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def test_conv2d_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, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_data_parallel_invalid_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 = ((8, 1, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=(2, 2, 1, 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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def test_conv2d_data_parallel_dilation():
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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, dilation=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_data_parallel_group():
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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(_w4, out_channel=8, kernel_size=2, pad_mode="same", stride=1, group=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_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 = ((8, 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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compile_net(net)
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def test_conv2d_model_parallel_dilation():
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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 = ((8, 1, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, dilation=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_conv2d_model_parallel_group():
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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 = ((8, 1, 1, 1),)
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net = Net(_w4, out_channel=8, kernel_size=2, pad_mode="same", stride=1, group=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_conv2d_model_parallel2():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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strategy1 = ((2, 2, 2, 2), (2, 2, 1, 1))
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strategy2 = ((32, 1, 1, 1),)
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=2, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_model_parallel3():
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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, 1, 1, 4), (1, 1, 1, 1))
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strategy2 = ((2, 1, 1, 4),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_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(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1)
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compile_net(net)
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def test_conv2d_model_parallel4():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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strategy1 = ((2, 2, 1, 4), (2, 2, 1, 1))
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strategy2 = ((2, 2, 1, 4),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_left_and_right_no_need_to_send():
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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, 1, 1, 4), (1, 1, 1, 1))
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strategy2 = ((2, 1, 1, 4),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=2, 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_conv2d_kernel_size_larger_than_stride_and_split_h():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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strategy1 = ((2, 2, 4, 1), (2, 2, 1, 1))
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strategy2 = ((2, 2, 4, 1),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1, 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_conv2d_valid_mode_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 = ((2, 1, 1, 2), (1, 1, 1, 1))
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strategy2 = ((2, 1, 1, 4),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="valid", stride=1, 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_conv2d_output_can_not_divisible_by_strategy():
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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))
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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=2, 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_conv2d_output_can_not_divisible_by_strategy2():
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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, 8, 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=2, 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_split_kernel():
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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, 1), (1, 1, 2, 2))
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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=2, 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_kernel_size_smaller_than_stride_and_slice_can_not_divisible_by_stride_same_mode():
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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, 2), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 8),)
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net = Net(_w0, out_channel=8, kernel_size=1, pad_mode="same", stride=3, strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x2)
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def test_kernel_size_smaller_than_stride_and_slice_can_not_divisible_by_stride_valid_mode():
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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, 2), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 8),)
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net = Net(_w0, out_channel=8, kernel_size=1, pad_mode="valid", stride=3, strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x2)
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def test_h_dimension_kernel_size_smaller_than_stride_and_slice_is_not_divisible_by_stride_same_mode():
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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, 1), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 8),)
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net = Net(_w0, out_channel=8, kernel_size=1, pad_mode="same", stride=3, strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x2)
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def test_h_dimension_kernel_size_smaller_than_stride_and_slice_can_not_divisible_by_stride_valid_mode():
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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, 1), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 8),)
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net = Net(_w0, out_channel=8, kernel_size=1, pad_mode="valid", stride=3, strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x2)
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def test_split_h_dimension_and_pad_mode_is_pad():
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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, 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="pad", stride=2, 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_kernel_size_larger_than_stride_and_input_can_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 = ((1, 1, 1, 2), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 8),)
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net = Net(_w3, out_channel=8, kernel_size=5, pad_mode="same", stride=3, strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x2)
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def test_kernel_size_larger_than_stride_and_slice_too_small():
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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))
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strategy2 = ((1, 1, 1, 8),)
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net = Net(_w3, out_channel=8, kernel_size=5, pad_mode="same", stride=1, 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_conv2d_same_mode_overlap_size_equal_to_slice_shape():
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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))
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strategy2 = ((2, 1, 1, 4),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1, 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_kernel_size_larger_than_stride_and_left_pad_is_0():
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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, 4), (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, strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net)
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