wehub-resource-sync
130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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 os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import paddle
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from paddle import base
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from paddle.distributed.fleet import fleet
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from paddle.distributed.fleet.base import role_maker
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class SparseLoadOp(unittest.TestCase):
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"""Test load operator."""
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def net(self, emb_array, fc_array):
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with base.unique_name.guard():
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dense_input = paddle.static.data(
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'input', shape=[None, 1], dtype="int64"
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)
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emb = paddle.nn.Embedding(
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num_embeddings=10,
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embedding_dim=10,
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sparse=True,
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weight_attr=base.ParamAttr(
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name="embedding",
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initializer=paddle.nn.initializer.Assign(emb_array),
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),
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)(dense_input)
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linear = paddle.nn.Linear(
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in_features=emb.shape[-1],
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out_features=10,
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weight_attr=base.ParamAttr(
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name='fc',
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initializer=paddle.nn.initializer.Assign(fc_array),
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),
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)(emb)
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fc1 = paddle.nn.ReLU()(linear)
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loss = paddle.mean(fc1)
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return loss
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def save_origin_model(self, emb_array, fc_array):
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startup_program = base.framework.Program()
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test_program = base.framework.Program()
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with (
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base.framework.program_guard(test_program, startup_program),
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base.unique_name.guard(),
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):
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loss = self.net(emb_array, fc_array)
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optimizer = paddle.optimizer.Adam(1e-3)
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optimizer.minimize(loss)
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exe = base.Executor(base.CPUPlace())
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exe.run(startup_program)
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model_path = tempfile.mkdtemp()
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paddle.distributed.io.save_persistables(
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executor=exe, dirname=model_path
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)
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return model_path
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@unittest.skip(reason="Skip unstable ut, need rewrite with new implement")
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class TestSparseLoadOpCase1(SparseLoadOp):
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def test_2ps_0_load(self):
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# init No.0 server env
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env = {}
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env["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:4001,127.0.0.1:4002"
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env["PADDLE_TRAINERS_NUM"] = str(2)
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env["TRAINING_ROLE"] = "PSERVER"
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env["PADDLE_PORT"] = "4001"
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env["POD_IP"] = "127.0.0.1"
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for k, v in env.items():
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os.environ[k] = str(v)
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"""
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array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
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[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
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[0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
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[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
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[0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],
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[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],
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[0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6, 0.6],
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[0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7],
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[0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
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[0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]])
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"""
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emb_array = np.arange(0, 1, 0.1).repeat(10).reshape(10, 10)
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fc_array = np.arange(0, 1, 0.1).repeat(10).reshape(10, 10)
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model_path = self.save_origin_model(emb_array, fc_array)
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role = role_maker.PaddleCloudRoleMaker()
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fleet.init(role)
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loss = self.net(emb_array, fc_array)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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optimizer = paddle.optimizer.Adam(1e-3)
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(loss)
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fleet.init_server(model_path)
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fc_w = np.array(base.global_scope().find_var("fc").get_tensor())
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emb = np.array(
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base.global_scope().find_var("embedding.block0").get_tensor()
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)
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assert fc_w.all() == fc_array.all()
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assert emb.all() == emb_array[::2].all()
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shutil.rmtree(model_path)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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