forked from huawei/mindspore2022
109 lines
4.4 KiB
Python
109 lines
4.4 KiB
Python
# Copyright 2019 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.nn as nn
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import mindspore as ms
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from mindspore import Tensor, context, Parameter
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from mindspore.common.api import _cell_graph_executor
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from mindspore.ops import operations as P
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from mindspore.common.initializer import initializer
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from mindspore.context import _Context
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from ....train_step_wrap import train_step_with_loss_warp
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class MatMulCell(nn.Cell):
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def __init__(self):
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super(MatMulCell, self).__init__()
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self.reshape = P.Reshape()
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self.matmul0 = P.MatMul(transpose_b=True)
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self.weight = Parameter(initializer("ones", [64, 128], ms.float32), name="weight")
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self.relu = P.ReLU().shard(((1, 8),))
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def construct(self, x):
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x = self.matmul0(x, self.weight)
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x = self.reshape(x, (32, 128))
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x = self.relu(x)
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return x
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class DenseMutMulNet(nn.Cell):
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def __init__(self, mp_comm_recompute=True, recompute_slice_activation=False):
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super(DenseMutMulNet, self).__init__()
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self.fc1 = nn.Dense(128, 768, activation='relu')
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self.fc2 = nn.Dense(128, 768, activation='relu')
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self.fc3 = nn.Dense(128, 768, activation='relu')
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self.fc4 = nn.Dense(768, 768, activation='relu')
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self.fc1.matmul.shard(((1, 1), (8, 1)))
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self.fc2.matmul.shard(((1, 1), (8, 1)))
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self.fc3.matmul.shard(((1, 1), (8, 1)))
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self.relu4 = nn.ReLU()
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self.relu5 = nn.ReLU()
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self.transpose = P.Transpose()
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self.matmul1 = P.MatMul()
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self.matmul2 = P.MatMul()
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self.matmul_cell = MatMulCell()
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self.fc1.recompute(mp_comm_recompute=mp_comm_recompute, recompute_slice_activation=recompute_slice_activation)
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self.fc2.recompute(mp_comm_recompute=mp_comm_recompute, recompute_slice_activation=recompute_slice_activation)
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self.fc3.recompute(mp_comm_recompute=mp_comm_recompute, recompute_slice_activation=recompute_slice_activation)
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self.matmul_cell.recompute(mp_comm_recompute=mp_comm_recompute,
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recompute_slice_activation=recompute_slice_activation)
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def construct(self, x):
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x = self.matmul_cell(x)
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q = self.fc1(x)
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k = self.fc2(x)
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v = self.fc3(x)
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k = self.transpose(k, (1, 0))
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c = self.relu4(self.matmul1(q, k))
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s = self.relu5(self.matmul2(c, v))
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s = self.fc4(s)
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return s
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def compile_net(mp_comm_recompute, recompute_slice_activation):
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context.reset_auto_parallel_context()
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_Context().set_backend_policy("vm")
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context.set_context(mode=context.GRAPH_MODE)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8)
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input_ = Tensor(np.ones([64, 128]).astype(np.float32) * 0.01)
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label = Tensor(np.zeros([32, 768]).astype(np.float32))
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net = train_step_with_loss_warp(DenseMutMulNet(mp_comm_recompute=mp_comm_recompute,
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recompute_slice_activation=recompute_slice_activation))
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, input_, label)
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_Context().set_backend_policy("ge")
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def test_dmnet_train_step_mp_recompute():
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"""
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Feature: test recompute interface.
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Description: test model parallel communication not recompute.
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Expectation: compile without error.
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"""
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compile_net(False, False)
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def test_dmnet_train_step_recompute_activation_slice():
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"""
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Feature: test recompute interface.
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Description: test slicing recompute cell output.
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Expectation: compile without error.
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"""
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compile_net(True, True)
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def test_dmnet_train_step_mp_recompute_recompute_activation_slice():
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"""
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Feature: test recompute interface.
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Description: test model parallel communication not recompute and slicing recompute cell output.
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Expectation: compile without error.
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"""
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compile_net(False, True)
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