forked from huawei/mindspore2022
287 lines
11 KiB
Python
287 lines
11 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,
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strategy1=None, strategy2=None):
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super().__init__()
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self.conv2d_transpose = P.Conv2DTranspose(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.neg = P.Neg().shard(strategy2)
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self.weight = Parameter(conv2d_weight, "w1")
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self.add = P.Add()
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self.add_w = Parameter(Tensor(np.ones([32, 8, 8, 8]), dtype=ms.float32), "add_w")
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def construct(self, x, b):
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out = self.add(x, self.add_w)
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out = self.conv2d_transpose(out, self.weight, (32, 16, 8, 8))
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out = self.neg(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, pad=0, group=1, dilation=1,
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strategy1=None, strategy2=None):
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super().__init__()
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self.conv2d_transpose = P.Conv2DTranspose(out_channel=out_channel, kernel_size=kernel_size, pad_mode=pad_mode,
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stride=stride, pad=pad, group=group,
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dilation=dilation).shard(strategy1)
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self.neg = P.Neg().shard(strategy2)
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self.weight = Parameter(conv2d_weight, "w1")
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def construct(self, x, b):
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out = self.conv2d_transpose(x, self.weight, (32, 16, 16, 16))
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out = self.neg(out)
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return out
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_x = Tensor(np.ones([32, 8, 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([8, 16, 4, 4]), dtype=ms.float32)
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_w3 = Tensor(np.ones([8, 16, 10, 10]), dtype=ms.float32)
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_w4 = Tensor(np.ones([8, 16, 3, 3]), dtype=ms.float32)
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_w5 = Tensor(np.ones([8, 8, 4, 4]), dtype=ms.float32)
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_w6 = Tensor(np.ones([8, 16, 5, 5]), dtype=ms.float32)
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_w7 = Tensor(np.ones([8, 16, 1, 1]), dtype=ms.float32)
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_w8 = 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):
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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, _x, _b)
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context.reset_auto_parallel_context()
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def test_conv2d_transpose_data_parallel():
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"""
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Feature: test 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, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_group():
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"""
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Feature: test group is not 1
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Description: shard n/h/w, and group is 2
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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 = Net2(_w5, out_channel=8, kernel_size=4, pad_mode="same", stride=2, group=2, strategy1=strategy1,
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strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_model_parallel1():
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"""
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Feature: test model parallel strategy
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Description: only shard batch dimension and channel 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 = ((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_transpose_model_parallel2():
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"""
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Feature: test model parallel strategy
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Description: shard batch dimension and w 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 = ((2, 1, 1, 4), (1, 1, 1, 1))
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strategy2 = ((2, 1, 1, 4),)
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net = Net2(_w2, out_channel=8, kernel_size=(4, 4), pad_mode="same", stride=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_model_parallel_dilation():
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"""
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Feature: test model parallel strategy and dilation is 2
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Description: shard n/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 = ((2, 1, 2, 2), (1, 1, 1, 1))
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strategy2 = ((2, 1, 2, 2),)
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net = Net2(_w4, out_channel=8, kernel_size=(3, 3), pad_mode="same", stride=2, dilation=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_model_parallel3():
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"""
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Feature: test model parallel strategy
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Description: shard batch dimension, channel dimension and w 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=16, global_rank=0)
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strategy1 = ((2, 2, 1, 4), (2, 1, 1, 1))
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strategy2 = ((2, 2, 1, 4),)
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net = Net2(_w2, out_channel=8, kernel_size=(4, 4), pad_mode="same", stride=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_model_parallel4():
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"""
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Feature: test model parallel strategy
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Description: shard h dimension and w 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=16, global_rank=0)
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strategy1 = ((1, 1, 2, 4), (1, 1, 1, 1))
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strategy2 = ((2, 2, 1, 4),)
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net = Net2(_w2, out_channel=8, kernel_size=(4, 4), pad_mode="same", stride=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_all_rank_no_need_overlap():
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"""
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Feature: test model parallel strategy
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Description: shard batch dimension, channel dimension and w 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=16, global_rank=0)
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strategy1 = ((2, 2, 1, 4), (2, 1, 1, 1))
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strategy2 = ((2, 2, 1, 4),)
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net = Net2(_w1, out_channel=8, kernel_size=(2, 2), pad_mode="same", stride=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_split_h_or_w_in_pad_mode():
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"""
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Feature: test pad mode
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Description: shard batch dimension, channel dimension and w dimension in pad mode
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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, 4), (2, 1, 1, 1))
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strategy2 = ((2, 2, 1, 4),)
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net = Net2(_w1, out_channel=8, kernel_size=(2, 2), pad_mode="pad", stride=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_split_h_in_same_mode():
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"""
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Feature: test split h dimension
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Description: shard h dimension in same mode
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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, 4, 1), (2, 1, 1, 1))
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strategy2 = ((2, 2, 4, 1),)
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net = Net2(_w1, out_channel=8, kernel_size=(2, 2), pad_mode="same", stride=2,
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strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_overlap_size_too_large():
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"""
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Feature: test overlap size is too large
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Description: shard w dimension and overlap size larger than slice shape
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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 = Net2(_w3, out_channel=8, kernel_size=(10, 10), pad_mode="same", stride=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_transpose_pad_mode_no_need_exchange():
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"""
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Feature: pad mode, and two direction send, w = 8, o = 16, s = 2, k = 1, n = 8, pad = (0, 0, 0, 0)
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Description: shard h and w 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=64, global_rank=13)
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strategy1 = ((1, 1, 8, 8), (1, 1, 1, 1))
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strategy2 = ((8, 1, 1, 1),)
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net = Net2(_w7, out_channel=8, kernel_size=1, pad_mode="pad", pad=(0, 0, 0, 0), stride=2, strategy1=strategy1,
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strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_pad_mode_two_direction_send_all_slice_pad_different():
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"""
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Feature: pad mode, and two direction send, w = 8, o = 16, s = 2, k = 5, n = 8, pad = (1, 2, 1, 2)
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Description: shard h and w 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=64, global_rank=13)
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strategy1 = ((1, 1, 8, 8), (1, 1, 1, 1))
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strategy2 = ((8, 1, 1, 1),)
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net = Net2(_w6, out_channel=8, kernel_size=5, pad_mode="pad", pad=(1, 2, 1, 2), stride=2, strategy1=strategy1,
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strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_pad_mode_two_direction_send_all_slice():
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"""
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Feature: pad mode, and two direction send, w = 8, o = 16, s = 2, k = 4, n = 8, pad = (1, 1, 1, 1)
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Description: shard h and w 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=64, global_rank=13)
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strategy1 = ((1, 1, 8, 8), (1, 1, 1, 1))
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strategy2 = ((8, 1, 1, 1),)
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net = Net2(_w8, out_channel=8, kernel_size=4, pad_mode="pad", pad=(1, 1, 1, 1), stride=2, strategy1=strategy1,
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strategy2=strategy2)
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compile_net(net)
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def test_conv2d_transpose_pad_mode_single_direction_send():
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"""
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Feature: pad mode, and single direction send, w = 8, o = 16, s = 2, k = 3, n = 8, pad = (0, 1, 0, 1)
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Description: shard h and w 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=64, global_rank=13)
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strategy1 = ((1, 1, 8, 8), (1, 1, 1, 1))
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strategy2 = ((8, 1, 1, 1),)
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net = Net2(_w4, out_channel=8, kernel_size=3, pad_mode="pad", pad=(0, 1, 0, 1), stride=2, strategy1=strategy1,
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strategy2=strategy2)
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compile_net(net)
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