wehub-resource-sync
150 lines
4.6 KiB
Python
150 lines
4.6 KiB
Python
# Copyright (c) 2019 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 math
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import os
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import sys
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import time
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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.base import compiler, core
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# open eager delete mode
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os.environ['FLAGS_eager_delete_tensor_gb'] = '0.0'
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os.environ['FLAGS_fast_eager_deletion_mode'] = 'true'
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os.environ['CPU_NUM'] = '2'
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class BuildIrMemOptBase(unittest.TestCase):
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def setup_reader(self):
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self.batch_size = 32
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self.word_dict = paddle.dataset.imdb.word_dict()
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self.train_reader = paddle.batch(
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paddle.dataset.imdb.train(self.word_dict),
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batch_size=self.batch_size,
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)
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def check_network_convergence(
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self,
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network,
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use_cuda=True,
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use_ir_memory_optimize=True,
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enable_inplace=True,
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iter=5,
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):
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if use_cuda and not core.is_compiled_with_cuda():
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print('Skip use_cuda=True because Paddle is not compiled with cuda')
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return
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if os.name == 'nt':
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print(
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'Skip use_parallel_executor=True because Paddle comes without parallel support on windows'
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)
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return
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paddle.seed(100)
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data = paddle.static.data(name="words", shape=[-1, 1], dtype="int64")
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label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64")
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cost = network(data, label, len(self.word_dict))
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optimizer = paddle.optimizer.Adam(learning_rate=0.001)
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optimizer.minimize(cost)
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build_strategy = base.BuildStrategy()
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build_strategy.enable_inplace = enable_inplace
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build_strategy.memory_optimize = use_ir_memory_optimize
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# execution
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place = base.CUDAPlace(0) if use_cuda else base.CPUPlace()
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feeder = base.DataFeeder(feed_list=[data, label], place=place)
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reader = feeder.feed(self.train_reader())
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exe = base.Executor(place)
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exe.run(base.default_startup_program())
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train_cp = compiler.CompiledProgram(
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base.default_main_program(), build_strategy=build_strategy
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)
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fetch_list = [cost.name]
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begin = time.time()
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first_loss, last_loss = None, None
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step_id = 0
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custom_iter = getattr(self, "iter", None)
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if custom_iter is not None:
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iter = custom_iter
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for data in reader():
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ret = exe.run(train_cp, feed=data, fetch_list=fetch_list)
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print(ret)
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step_id += 1
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if step_id == 1:
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first_loss = ret[0]
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if step_id == iter:
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last_loss = ret[0]
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break
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end = time.time()
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print(
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"%.4f Instance per second"
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% ((self.batch_size * iter) / (end - begin))
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)
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print(first_loss, last_loss)
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avg_last_loss_val = np.array(last_loss).mean()
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avg_first_loss_val = np.array(first_loss).mean()
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if math.isnan(float(avg_last_loss_val)) or math.isnan(
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float(avg_first_loss_val)
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):
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sys.exit("got NaN loss, training failed.")
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return first_loss, last_loss
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class TestIrMemOptBase(BuildIrMemOptBase):
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def setUp(self):
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self.network = None
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def test_network(self):
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if self.network is None or not core.is_compiled_with_cuda():
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return
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self.setup_reader()
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with (
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base.program_guard(base.Program(), base.Program()),
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base.scope_guard(core.Scope()),
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):
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(
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baseline_first_loss,
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baseline_last_loss,
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) = self.check_network_convergence(self.network)
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cur_first_loss, cur_last_loss = self.check_network_convergence(
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self.network
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)
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self.assertAlmostEqual(
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np.mean(baseline_last_loss),
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np.mean(cur_last_loss),
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delta=1e-6,
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)
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self.assertAlmostEqual(
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np.mean(baseline_first_loss),
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np.mean(cur_first_loss),
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delta=1e-6,
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)
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