forked from mindspore-Ecosystem/mindspore
104 lines
3.5 KiB
Python
104 lines
3.5 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 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
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from mindspore.nn.optim.adafactor import AdaFactor
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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, add_weight, matmul_weight, bias, strategy1=None, strategy2=None):
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super().__init__()
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self.add = P.TensorAdd()
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self.matmul = P.MatMul().shard(strategy1)
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self.bias_add = P.BiasAdd().shard(strategy2)
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self.add_weight = Parameter(add_weight, "w1")
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self.mul_weight = Parameter(matmul_weight, "w1")
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self.bias = Parameter(bias, "bias")
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self.reshape = P.Reshape()
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def construct(self, x, b):
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out = self.add(x, self.add_weight)
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out = self.reshape(out, (64, 32))
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out = self.matmul(out, self.mul_weight)
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out = self.add(out, self.bias)
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return out
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_x = Tensor(np.ones([64, 16, 2]), dtype=ms.float32)
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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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_b = Tensor(np.ones([64, 16, 2]), dtype=ms.float32)
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def compile_net(net):
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scale_parameter = False
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relative_step = True
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warmup_init = True
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compression = True
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optimizer = AdaFactor(net.trainable_params(), learning_rate=None, weight_decay=0.9,
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scale_parameter=scale_parameter, relative_step=relative_step,
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warmup_init=warmup_init, compression=compression)
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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_opt_data_parallel():
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"""
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Feature: test adafactor data parallel
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Description:
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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 = ((16, 1), (1, 1))
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strategy2 = ((16, 1), (1,))
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net = Net(_w0, _w1, _w2, strategy1, strategy2)
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compile_net(net)
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def test_opt_model_parallel():
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"""
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Feature: test adafactor model parallel
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Description:
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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 = ((4, 2), (2, 2))
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strategy2 = ((4, 2), (2,))
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net = Net(_w0, _w1, _w2, strategy1, strategy2)
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compile_net(net)
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def test_opt_shard():
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"""
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Feature: test adafactor optimizer parallel
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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=16, global_rank=0,
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enable_parallel_optimizer=True)
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strategy1 = ((4, 2), (2, 2))
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strategy2 = ((4, 2), (2,))
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net = Net(_w0, _w1, _w2, strategy1, strategy2)
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compile_net(net)
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