498 lines
19 KiB
Python
498 lines
19 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, pad=0,
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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, 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.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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_x3 = Tensor(np.ones([32, 16, 16, 16]), 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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_w5 = Tensor(np.ones([8, 16, 4, 4]), 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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"""
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Feature: test conv2d 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))
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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_pad_mode_overlap_is_negative():
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"""
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Feature: test conv2d pad mode and overlap is negative
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Description: shard 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 = ((1, 1, 4, 4), (1, 1, 1, 1))
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strategy2 = ((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),
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strategy1=strategy1, strategy2=strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, _x3)
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def test_conv2d_pad_mode():
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"""
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Feature: test conv2d pad mode and overlap is non-negative
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Description: shard 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=8, global_rank=0)
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strategy1 = ((1, 1, 2, 4), (1, 1, 1, 1))
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strategy2 = ((1, 1, 1, 1),)
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net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="pad", stride=1, pad=(3, 3, 3, 3),
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net, _x3)
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def test_conv2d_valid_mode_output_shape_cannot_div_by_strategy():
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"""
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Feature: test conv2d valid mode, and output shape can not div by strategy
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Description: shard 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=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, 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)
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def test_conv2d_data_parallel_invalid_stride():
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"""
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Feature: test conv2d invalid stride
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Description: the first two elements of stride must be 1, but set 2
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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 = ((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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"""
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Feature: test conv2d data parallel and dilation is not 1
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Description: data parallel and dilation is not 1
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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, 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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"""
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Feature: test conv2d data parallel and group is not 1
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Description: data parallel and group is not 1
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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(_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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"""
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Feature: test conv2d model parallel
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Description: split n/c-in/c-out
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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 = ((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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"""
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Feature: test conv2d model parallel and dilation is not 1
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Description: model parallel and dilation is not 1
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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 = ((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_model_parallel_group():
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"""
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Feature: test conv2d model parallel and group is not 1
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Description: split cin and cout, and group is not 1
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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 = ((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_parallel_group2():
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"""
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Feature: test conv2d model parallel and group is not 1
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Description: has not to split cin and cout, and group is not 1
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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, 1, 2, 2), (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_parallel2():
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"""
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Feature: same mode, stride = kernel_size, no need exchange
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Description: split n/c-in/c-out/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=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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"""
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Feature: same mode, stride < kernel_size, need exchange
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Description: split n/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=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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"""
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Feature: same mode, auto parallel
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Description: generate data parallel strategy
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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(_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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"""
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Feature: same mode, stride < kernel_size, need exchange
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Description: split n/c-in/c-out/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=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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"""
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Feature: same mode, k - s = 1, left pad is 0, single direction exchange
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Description: support that the left no need to send
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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, 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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compile_net(net)
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def test_conv2d_kernel_size_larger_than_stride_and_split_h():
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"""
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Feature: same mode, stride < kernel_size, need exchange
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Description: split n/c-in/c-out/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=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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compile_net(net)
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def test_conv2d_valid_mode_kernel_size_larger_than_stride():
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"""
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Feature: valid mode, stride < kernel_size, need exchange
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Description: do not support to split 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=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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"""
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Feature: same mode, stride = kernel_size, but output shape can not be divided by strategy
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Description: split w dimension
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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))
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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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"""
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Feature: same mode, stride = kernel_size, but output shape can not be divided by strategy
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Description: split h dimension
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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, 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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"""
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Feature: split kernel size
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Description: do not support to split kernel size
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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, 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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"""
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Feature: same mode, slice shape can not be divided by stride
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Description: split 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=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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"""
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Feature: valid mode, slice shape can not be divided by stride
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Description: split 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=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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"""
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Feature: same mode, slice shape can not be divided by stride
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Description: split 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=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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"""
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Feature: valid mode, slice shape can not be divided by stride
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Description: split 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=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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"""
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Feature: pad mode
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Description: split 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=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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|
|
|
|
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def test_kernel_size_larger_than_stride_and_input_can_not_divisible_by_stride():
|
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"""
|
|
Feature: same mode, input shape can not be divided by stride
|
|
Description: split w
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|
Expectation: compile failed
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 1, 1, 2), (1, 1, 1, 1))
|
|
strategy2 = ((1, 1, 1, 8),)
|
|
net = Net(_w3, out_channel=8, kernel_size=5, pad_mode="same", stride=3, strategy1=strategy1, strategy2=strategy2)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net, _x2)
|
|
|
|
|
|
def test_kernel_size_larger_than_stride_and_slice_too_small():
|
|
"""
|
|
Feature: same mode, slice shape is small than overlap shape
|
|
Description: split w
|
|
Expectation: compile failed
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 1, 1, 8), (1, 1, 1, 1))
|
|
strategy2 = ((1, 1, 1, 8),)
|
|
net = Net(_w3, out_channel=8, kernel_size=5, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
|
|
with pytest.raises(RuntimeError):
|
|
compile_net(net)
|
|
|
|
|
|
def test_conv2d_dilation():
|
|
"""
|
|
Feature: same mode, dilation is 2
|
|
Description: split n/h/w
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((2, 1, 2, 2), (1, 1, 1, 1))
|
|
strategy2 = ((2, 2, 1, 2),)
|
|
net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1, dilation=2, strategy1=strategy1,
|
|
strategy2=strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_conv2d_same_mode_overlap_size_equal_to_slice_shape():
|
|
"""
|
|
Feature: same mode, slice shape is equal to overlap shape
|
|
Description: split w
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 1, 1, 8), (1, 1, 1, 1))
|
|
strategy2 = ((2, 1, 1, 4),)
|
|
net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_kernel_size_larger_than_stride_and_left_pad_is_0():
|
|
"""
|
|
Feature: same mode, kernel_size > stride and left pad is 0, single direction exchange
|
|
Description: split w
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
|
strategy1 = ((1, 1, 1, 4), (1, 1, 1, 1))
|
|
strategy2 = ((1, 1, 1, 8),)
|
|
net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
|
|
compile_net(net)
|
|
|
|
|
|
def test_conv2d_kernel_size_larger_than_stride_and_split_nchw():
|
|
"""
|
|
Feature: same mode, stride < kernel_size, need exchange
|
|
Description: split n/c-in/c-out/h/w
|
|
Expectation: compile success
|
|
"""
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
|
|
strategy1 = ((2, 2, 2, 2), (2, 2, 1, 1))
|
|
strategy2 = ((2, 2, 2, 2),)
|
|
net = Net(_w2, out_channel=8, kernel_size=3, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
|
|
compile_net(net)
|