chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2018 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 paddle
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paddle.enable_static()
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from dist_fleet_sparse_embedding_ctr import fake_ctr_reader
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from test_dist_fleet_base import TestFleetBase
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from paddle import base
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@unittest.skip(reason="Skip unstable ut, need paddle sync mode fix")
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class TestDistMnistSync2x2(TestFleetBase):
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def _setup_config(self):
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self._mode = "sync"
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self._reader = "pyreader"
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def check_with_place(
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self, model_file, delta=1e-3, check_error_log=False, need_envs={}
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):
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": "",
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"CPU_NUM": "2",
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"LOG_DIRNAME": "/tmp",
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"LOG_PREFIX": self.__class__.__name__,
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_sparse_embedding_ctr.py",
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delta=1e-5,
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check_error_log=True,
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)
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class TestDistMnistAsync2x2(TestFleetBase):
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def _setup_config(self):
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self._mode = "async"
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self._reader = "pyreader"
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def check_with_place(
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self, model_file, delta=1e-3, check_error_log=False, need_envs={}
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):
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": "",
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"CPU_NUM": "2",
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"LOG_DIRNAME": "/tmp",
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"LOG_PREFIX": self.__class__.__name__,
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_sparse_embedding_ctr.py",
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delta=1e-5,
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check_error_log=True,
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)
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class TestDistMnistAsync2x2WithDecay(TestFleetBase):
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def _setup_config(self):
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self._mode = "async"
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self._reader = "pyreader"
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def check_with_place(
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self, model_file, delta=1e-3, check_error_log=False, need_envs={}
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):
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": "",
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"CPU_NUM": "2",
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"DECAY": "0",
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"LOG_DIRNAME": "/tmp",
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"LOG_PREFIX": self.__class__.__name__,
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_sparse_embedding_ctr.py",
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delta=1e-5,
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check_error_log=True,
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)
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class TestDistMnistAsync2x2WithUniform(TestFleetBase):
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def _setup_config(self):
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self._mode = "async"
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self._reader = "pyreader"
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def check_with_place(
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self, model_file, delta=1e-3, check_error_log=False, need_envs={}
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):
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": "",
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"CPU_NUM": "2",
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"INITIALIZER": "1",
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"LOG_DIRNAME": "/tmp",
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"LOG_PREFIX": self.__class__.__name__,
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_sparse_embedding_ctr.py",
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delta=1e-5,
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check_error_log=True,
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)
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@unittest.skip(reason="Skip unstable ut, need tensor table to enhance")
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class TestDistMnistAsync2x2WithGauss(TestFleetBase):
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def _setup_config(self):
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self._mode = "async"
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self._reader = "pyreader"
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def _run_local_infer(self, model_file):
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def net():
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"""
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network definition
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Args:
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batch_size(int): the size of mini-batch for training
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lr(float): learning rate of training
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Returns:
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avg_cost: DenseTensor of cost.
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"""
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dnn_input_dim, lr_input_dim = 10, 10
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dnn_data = paddle.static.data(
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name="dnn_data",
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shape=[-1, 1],
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dtype="int64",
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)
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lr_data = paddle.static.data(
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name="lr_data",
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shape=[-1, 1],
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dtype="int64",
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)
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label = paddle.static.data(
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name="click",
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shape=[-1, 1],
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dtype="int64",
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)
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datas = [dnn_data, lr_data, label]
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inference = True
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init = paddle.nn.initializer.Uniform()
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dnn_layer_dims = [128, 64, 32]
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dnn_embedding = paddle.static.nn.sparse_embedding(
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input=dnn_data,
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size=[dnn_input_dim, dnn_layer_dims[0]],
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is_test=inference,
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param_attr=base.ParamAttr(
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name="deep_embedding", initializer=init
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),
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)
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dnn_pool = paddle.static.nn.sequence_lod.sequence_pool(
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input=dnn_embedding, pool_type="sum"
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)
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dnn_out = dnn_pool
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for i, dim in enumerate(dnn_layer_dims[1:]):
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fc = paddle.static.nn.fc(
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x=dnn_out,
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size=dim,
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activation="relu",
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.01)
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),
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name=f'dnn-fc-{i}',
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)
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dnn_out = fc
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# build lr model
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lr_embedding = paddle.static.nn.sparse_embedding(
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input=lr_data,
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size=[lr_input_dim, 1],
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is_test=inference,
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param_attr=base.ParamAttr(
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name="wide_embedding",
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initializer=paddle.nn.initializer.Constant(value=0.01),
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),
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)
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lr_pool = paddle.static.nn.sequence_lod.sequence_pool(
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input=lr_embedding, pool_type="sum"
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)
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merge_layer = paddle.concat([dnn_out, lr_pool], axis=1)
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predict = paddle.static.nn.fc(
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x=merge_layer, size=2, activation='softmax'
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)
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return datas, predict
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reader = paddle.batch(fake_ctr_reader(), batch_size=4)
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datas, predict = net()
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exe = base.Executor(base.CPUPlace())
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feeder = base.DataFeeder(place=base.CPUPlace(), feed_list=datas)
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exe.run(base.default_startup_program())
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paddle.distributed.io.load_persistables(exe, model_file)
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for batch_id, data in enumerate(reader()):
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score = exe.run(
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base.default_main_program(),
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feed=feeder.feed(data),
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fetch_list=[predict],
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)
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def check_with_place(
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self, model_file, delta=1e-3, check_error_log=False, need_envs={}
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):
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model_dir = tempfile.mkdtemp()
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"FLAGS_rpc_deadline": "5000", # 5sec to fail fast
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"http_proxy": "",
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"CPU_NUM": "2",
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"INITIALIZER": "2",
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"MODEL_DIR": model_dir,
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"LOG_DIRNAME": "/tmp",
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"LOG_PREFIX": self.__class__.__name__,
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}
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required_envs.update(need_envs)
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if check_error_log:
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required_envs["GLOG_v"] = "3"
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required_envs["GLOG_logtostderr"] = "1"
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self._run_cluster(model_file, required_envs)
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self._run_local_infer(model_dir)
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shutil.rmtree(model_dir)
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def test_dist_train(self):
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self.check_with_place(
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"dist_fleet_sparse_embedding_ctr.py",
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delta=1e-5,
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check_error_log=True,
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)
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if __name__ == "__main__":
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unittest.main()
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