forked from huawei/mindspore2022
239 lines
9.7 KiB
Python
239 lines
9.7 KiB
Python
# Copyright 2020 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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# ============================================================================
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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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import mindspore.nn as nn
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from mindspore import context
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from mindspore.common.parameter import Parameter
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from mindspore.common.initializer import initializer
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from mindspore.train.model import Model
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from mindspore.nn.wrap.cell_wrapper import PipelineCell
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from mindspore.parallel._auto_parallel_context import auto_parallel_context
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from tests.ut.python.parallel.test_adafactor import compile_net
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from tests.ut.python.parallel.test_adafactor import Net as Net2
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class DatasetLenet():
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def __init__(self, data, label, length=3):
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self.data = data
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self.label = label
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self.index = 1
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.data, self.label
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def reset(self):
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self.index = 0
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@staticmethod
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def get_dataset_size():
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return 32
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@staticmethod
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def get_repeat_count():
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return 1
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@staticmethod
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def get_batch_size():
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return 32
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def create_tuple_iterator(self, num_epochs=1, do_copy=True):
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return self
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class MatMulCell(nn.Cell):
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def __init__(self, strategy1, strategy2, param=None):
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super().__init__()
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self.param = Parameter(initializer("zeros", [64, 64]), name="param")
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if param is not None:
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self.param = param
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self.param1 = Parameter(initializer("zeros", [64, 64]), name="param1")
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self.matmul = P.MatMul().shard(strategy1)
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self.matmul1 = P.MatMul().shard(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.param)
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out = self.matmul1(out, self.param1)
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return out
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, param=None):
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super().__init__()
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self.block = nn.CellList()
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for i in range(2):
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cell = MatMulCell(strategy1, strategy2, param)
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cell.pipeline_stage = i
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self.block.append(cell)
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def construct(self, x):
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for i in range(2):
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x = self.block[i](x)
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return x
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class PipelineSplit(nn.Cell):
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def __init__(self, strategy1, strategy2):
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super().__init__()
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self.cell = Net(strategy1, strategy2)
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self.cell.block[0].matmul.add_prim_attr("parameter_start", 0)
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def construct(self, x, label):
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x = self.cell(x)
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return x
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def test_fusion_size():
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"""
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Feature: test_fusion_auto in size mode
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Description: allgather and reduce scatter fusion in size mode
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Expectation: success
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"""
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allgather_threshold = 8
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reducescatter_threshold = 16
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comm_fusion_dict = {"allgather": {"mode": "size", "config": allgather_threshold},
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"reducescatter": {"mode": "size", "config": reducescatter_threshold}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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context.set_auto_parallel_context(device_num=8, global_rank=0, pipeline_stages=2, enable_parallel_optimizer=True)
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64]), dtype=ms.float32)
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strategy1 = ((4, 1), (1, 1))
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strategy2 = ((2, 1), (1, 1))
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net = PipelineCell(PipelineSplit(strategy1, strategy2), 4)
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dataset = DatasetLenet(data, label, 3)
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optimizer = nn.Lamb(net.trainable_params(), learning_rate=0.01)
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model = Model(net, optimizer=optimizer)
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model.train(2, dataset, dataset_sink_mode=False)
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assert auto_parallel_context().allgather_fusion_threshold_mb() == allgather_threshold
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assert auto_parallel_context().reducescatter_fusion_threshold_mb() == reducescatter_threshold
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def test_fusion_auto():
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"""
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Feature: test_fusion_auto in auto mode
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Description: allgather and reduce scatter fusion in auto mode
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Expectation: success
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"""
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comm_fusion_dict = {"allgather": {"mode": "auto", "config": None},
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"reducescatter": {"mode": "auto", "config": None}}
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context.set_auto_parallel_context(device_num=8, global_rank=0, pipeline_stages=2, enable_parallel_optimizer=True,
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parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64]), dtype=ms.float32)
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strategy1 = ((4, 1), (1, 1))
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strategy2 = ((2, 1), (1, 1))
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net = PipelineCell(PipelineSplit(strategy1, strategy2), 4)
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dataset = DatasetLenet(data, label, 3)
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optimizer = nn.Lamb(net.trainable_params(), learning_rate=0.01)
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model = Model(net, optimizer=optimizer)
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model.train(2, dataset, dataset_sink_mode=False)
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assert auto_parallel_context().allgather_fusion_threshold_mb() == 64
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assert auto_parallel_context().reducescatter_fusion_threshold_mb() == 64
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def test_fusion_optimizer_parallel():
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"""
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Feature: test_fusion_optimizer_parallel in size mode
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Description: allgather and reduce scatter size fusion in optimizer parallel
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Expectation: compile success
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"""
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allgather_threshold = 16
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reducescatter_threshold = 8
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comm_fusion_dict = {"allgather": {"mode": "size", "config": allgather_threshold},
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"reducescatter": {"mode": "size", "config": reducescatter_threshold}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0,
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enable_parallel_optimizer=True, comm_fusion=comm_fusion_dict)
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_w0 = Tensor(np.ones([64, 16, 2]), dtype=ms.float32)
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_w1 = Tensor(np.ones([32, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([32]), dtype=ms.float32)
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strategy1 = ((4, 2), (2, 2))
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strategy2 = ((4, 2), (2,))
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net = Net2(_w0, _w1, _w2, strategy1, strategy2)
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compile_net(net)
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comm_fusion_dict = {"allgather": {"mode": "auto", "config": None},
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"reducescatter": {"mode": "auto", "config": None}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0,
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enable_parallel_optimizer=True, comm_fusion=comm_fusion_dict)
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compile_net(net)
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def test_allgather_fusion_invalid_value_failed():
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"""
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Feature: test_allgather_fusion with invalid value
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Description: test_allgather_fusion with invalid value
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Expectation: throw TypeError
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"""
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with pytest.raises(TypeError):
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comm_fusion_dict = [1, 2]
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(TypeError):
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comm_fusion_dict = {"allgather": [1, 2]}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(TypeError):
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comm_fusion_dict = {"allgather": {"mode": "size", "config": "30.12"}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(KeyError):
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comm_fusion_dict = {"all": {"mode": "size", "config": 30}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(KeyError):
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comm_fusion_dict = {"allgather": {"modes": "size", "config": 30}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(KeyError):
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comm_fusion_dict = {"allgather": {"mode": "sizes", "config": 30}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(KeyError):
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comm_fusion_dict = {"allgather": {"mode": "size"}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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def test_reducescatter_fusion_invalid_value_failed():
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"""
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Feature: test_reducescatter_fusion with invalid value
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Description: test_reducescatter_fusion with invalid value
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Expectation: throw TypeError
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"""
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with pytest.raises(TypeError):
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comm_fusion_dict = {"reducescatter": [1, 2]}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(TypeError):
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comm_fusion_dict = {"reducescatter": {"mode": "size", "config": "30.12"}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(KeyError):
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comm_fusion_dict = {"reducescatter": {"modes": "size", "config": 30}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(KeyError):
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comm_fusion_dict = {"reducescatter": {"mode": "sizes", "config": 30}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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with pytest.raises(KeyError):
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comm_fusion_dict = {"reducescatter": {"mode": "size"}}
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", comm_fusion=comm_fusion_dict)
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