forked from mindspore-Ecosystem/mindspore
141 lines
4.8 KiB
Python
141 lines
4.8 KiB
Python
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 import ParameterTuple
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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.train.model import Model
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from mindspore.nn.wrap.cell_wrapper import PipelineCell
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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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@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 reset(self):
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self.index = 0
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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 MatMulNet(nn.Cell):
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def __init__(self, strategy1, strategy2, matmul_weight):
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super().__init__()
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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.matmul_weight = Parameter(matmul_weight[0], name='weight1')
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self.matmul_weight2 = Parameter(matmul_weight[1], name='weight2')
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def construct(self, inputs):
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out = self.matmul(inputs, self.matmul_weight)
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out = self.matmul1(out, self.matmul_weight2)
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return out
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, matmul_weight):
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super().__init__()
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self.block = nn.CellList()
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self.add = P.TensorAdd()
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self.add_list = []
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self.relu_block = nn.CellList()
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for i in range(2):
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cell = MatMulNet(strategy1, strategy2, matmul_weight[i])
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cell.pipeline_stage = i
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self.block.append(cell)
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relu = nn.ReLU()
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relu.pipeline_stage = i
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self.relu_block.append(relu)
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self.add_list.append(
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Parameter(Tensor(np.full((1, 16), 0.1, dtype=np.float32)), name=f"weight{i}"))
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self.add_tuple = ParameterTuple(self.add_list)
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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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x = self.relu_block[i](x)
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x = self.add(x, self.add_tuple[i])
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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
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Description:pipeline remove monad nodes
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=16, global_rank=0, pipeline_stages=2, full_batch=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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weight = Tensor(0.1 * np.random.randn(96, 16).astype(np.float32))
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weight2 = Tensor(0.1 * np.random.randn(16, 16).astype(np.float32))
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weight3 = Tensor(0.1 * np.random.randn(16, 16).astype(np.float32))
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weight4 = Tensor(0.1 * np.random.randn(16, 16).astype(np.float32))
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weight_list = [[weight, weight2], [weight3, weight4]]
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data = Tensor(np.ones([32, 96]), dtype=ms.float32)
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label = Tensor(np.ones([32, 16]), dtype=ms.float32)
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strategy1 = ((2, 1), (1, 4))
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strategy2 = ((1, 2), (2, 2))
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net = Net(strategy1, strategy2, weight_list)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 3)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
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net = nn.WithLossCell(net, loss)
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net = PipelineCell(net, 4)
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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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def test_pipeline_split_stage1():
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"""
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Feature:pipeline stage1
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Description:pipeline remove monad nodes
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Expectation:success
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"""
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context.set_auto_parallel_context(device_num=16, global_rank=8, pipeline_stages=2, full_batch=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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weight = Tensor(0.1 * np.random.randn(96, 16).astype(np.float32))
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weight2 = Tensor(0.1 * np.random.randn(16, 16).astype(np.float32))
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weight3 = Tensor(0.1 * np.random.randn(16, 16).astype(np.float32))
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weight4 = Tensor(0.1 * np.random.randn(16, 16).astype(np.float32))
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weight_list = [[weight, weight2], [weight3, weight4]]
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data = Tensor(np.ones([128, 96]), dtype=ms.float32)
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label = Tensor(np.ones([128, 16]), dtype=ms.float32)
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strategy1 = ((2, 1), (1, 4))
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strategy2 = ((1, 2), (2, 2))
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net = Net(strategy1, strategy2, weight_list)
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params = net.trainable_params()
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dataset = DatasetLenet(data, label, 3)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=False)
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net = nn.WithLossCell(net, loss)
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net = PipelineCell(net, 4)
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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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