forked from huawei/mindspore2022
394 lines
16 KiB
Python
394 lines
16 KiB
Python
# Copyright 2022 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 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, MicroBatchInterleaved
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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, dtype=ms.float32):
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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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self.cast = P.Cast()
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self.dtype = dtype
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def construct(self, x):
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out = self.matmul(self.cast(x, self.dtype), self.cast(self.param, self.dtype))
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out = self.matmul1(out, self.cast(self.param1, self.dtype))
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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, dtype=ms.float32):
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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, dtype)
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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, dtype=ms.float32):
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super().__init__()
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self.cell = Net(strategy1, strategy2, dtype=dtype)
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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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class PipelineSplitSharedParam(nn.Cell):
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def __init__(self, strategy1, strategy2, dtype=ms.float32):
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super().__init__()
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self.param = Parameter(initializer("zeros", [64, 64]), name="param")
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self.cell = Net(strategy1, strategy2, self.param, dtype)
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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_pipeline_split_stage0():
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"""
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Feature:pipeline stage0 + opt detection
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=0, pipeline_stages=2)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplit(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 3)
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optimizer = nn.Lamb(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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for _, param in model._train_network.parameters_and_names():
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assert param.name != "cell.block.1.param"
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assert param.name != "cell.block.1.param1"
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def test_pipeline_split_stage1():
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"""
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Feature:pipeline stage1 + opt detection
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=16, pipeline_stages=2)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplit(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 4)
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optimizer = nn.Lamb(params, learning_rate=0.001)
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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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for _, param in model._train_network.parameters_and_names():
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assert param.name != "cell.block.0.param"
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assert param.name != "cell.block.0.param1"
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def test_pipeline_split_shared_parameter_stage0():
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"""
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Feature:pipeline stage0 + opt detection + shared parameter
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=0, pipeline_stages=2)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplitSharedParam(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 6)
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optimizer = nn.Lamb(params, learning_rate=0.03)
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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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def test_pipeline_split_shared_parameter_stage1():
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"""
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Feature:pipeline stage1 + opt detection + shared parameter
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=16, pipeline_stages=2)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplitSharedParam(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 7)
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optimizer = nn.Lamb(params, learning_rate=0.04)
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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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def test_pipeline_split_stage0_opt_shard():
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"""
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Feature:pipeline stage0 + opt detection + opt shard
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=0, pipeline_stages=2, enable_parallel_optimizer=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplit(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 6)
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optimizer = nn.Lamb(params, learning_rate=0.02)
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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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for _, param in model._train_network.parameters_and_names():
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assert param.name != "cell.block.1.param"
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assert param.name != "cell.block.1.param1"
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def test_pipeline_split_stage1_opt_shard():
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"""
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Feature:pipeline stage1 + opt detection + opt shard
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=16, pipeline_stages=2, enable_parallel_optimizer=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplit(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 8)
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optimizer = nn.Lamb(params, learning_rate=0.04)
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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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for _, param in model._train_network.parameters_and_names():
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assert param.name != "cell.block.0.param"
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assert param.name != "cell.block.0.param1"
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def test_pipeline_split_shared_parameter_stage0_opt_shard():
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"""
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Feature:pipeline stage0 + opt detection + opt shard + shared parameter
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=0, pipeline_stages=2, enable_parallel_optimizer=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplitSharedParam(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 2)
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optimizer = nn.Lamb(params, learning_rate=0.06)
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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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def test_pipeline_split_shared_parameter_stage1_opt_shard():
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"""
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Feature:pipeline stage1 + opt detection + opt shard + shared parameter
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Description:pipeline opt detection
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=16, pipeline_stages=2, enable_parallel_optimizer=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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net = PipelineCell(PipelineSplitSharedParam(strategy1, strategy2), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 9)
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optimizer = nn.Lamb(params, learning_rate=0.06)
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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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def test_pipeline_split_with_micro_batch_interleaved_stage0():
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"""
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Feature: test PipelineSplit with MicroBatchInterleaved in auto parallel.
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Description: net with MicroBatchInterleaved in semi auto parallel.
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Expectation: success.
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=0, pipeline_stages=2)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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micro_batch_interleaved = 2
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net = PipelineCell(MicroBatchInterleaved(PipelineSplit(strategy1, strategy2), micro_batch_interleaved), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 3)
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optimizer = nn.Lamb(params, learning_rate=0.07)
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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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for _, param in model._train_network.parameters_and_names():
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assert param.name != "cell.block.1.param"
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assert param.name != "cell.block.1.param1"
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def test_pipeline_split_with_micro_batch_interleaved_stage1():
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"""
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Feature: test PipelineSplit with MicroBatchInterleaved in auto parallel.
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Description: net with MicroBatchInterleaved in semi auto parallel.
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Expectation: success.
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=16, pipeline_stages=2)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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micro_batch_interleaved = 2
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net = PipelineCell(MicroBatchInterleaved(PipelineSplit(strategy1, strategy2), micro_batch_interleaved), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 3)
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optimizer = nn.Lamb(params, learning_rate=0.08)
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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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for _, param in model._train_network.parameters_and_names():
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assert param.name != "cell.block.0.param"
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assert param.name != "cell.block.0.param1"
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def test_pipeline_split_shared_parameter_with_micro_batch_interleaved_stage0_opt_shard():
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"""
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Feature: test PipelineSplitSharedParameter with MicroBatchInterleaved in auto parallel.
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Description: net with MicroBatchInterleaved in semi auto parallel.
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Expectation: success.
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=0, pipeline_stages=2, enable_parallel_optimizer=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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micro_batch_interleaved = 2
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net = PipelineCell(MicroBatchInterleaved(PipelineSplitSharedParam(strategy1, strategy2),
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micro_batch_interleaved), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 5)
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optimizer = nn.Lamb(params, learning_rate=0.06)
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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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def test_pipeline_split_shared_parameter_with_micro_batch_interleaved_stage1_opt_shard():
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"""
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Feature: test PipelineSplitSharedParameter with MicroBatchInterleaved in auto parallel.
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Description: net with MicroBatchInterleaved in semi auto parallel.
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Expectation: success.
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"""
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context.set_auto_parallel_context(device_num=32, global_rank=16, pipeline_stages=2, enable_parallel_optimizer=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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strategy1 = ((16, 1), (1, 1))
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strategy2 = ((8, 1), (1, 1))
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micro_batch_interleaved = 2
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net = PipelineCell(MicroBatchInterleaved(PipelineSplitSharedParam(strategy1, strategy2),
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micro_batch_interleaved), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 4)
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optimizer = nn.Lamb(params, learning_rate=0.02)
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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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def run_pipeline_split_function(pipeline_net, micro_batch_interleaved=1):
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"""
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Feature: test PipelineSplitSharedParameter with MicroBatchInterleaved in auto parallel.
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Description: net with MicroBatchInterleaved in semi auto parallel.
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Expectation: success.
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"""
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data = Tensor(np.ones([32, 64]), dtype=ms.float32)
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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = PipelineCell(MicroBatchInterleaved(pipeline_net, micro_batch_interleaved), 4)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 3)
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optimizer = nn.Lamb(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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