wehub-resource-sync
129 lines
4.6 KiB
Python
129 lines
4.6 KiB
Python
# 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 math
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import os
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import sys
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import tempfile
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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from simple_nets import simple_fc_net
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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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class TestPassBuilder(unittest.TestCase):
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def check_network_convergence(self, use_cuda, build_strategy=None):
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os.environ['CPU_NUM'] = str(4)
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main = base.Program()
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startup = base.Program()
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with base.program_guard(main, startup):
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loss = simple_fc_net()
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test_program = main.clone(for_test=True)
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opt = paddle.optimizer.SGD(learning_rate=0.001)
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opt.minimize(loss)
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batch_size = 32
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image = np.random.normal(size=(batch_size, 784)).astype('float32')
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label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
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place = get_device_place() if use_cuda else base.CPUPlace()
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exe = base.Executor(place)
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exe.run(startup)
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feed_dict = {'image': image, 'label': label}
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train_cp = compiler.CompiledProgram(
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main, build_strategy=build_strategy
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)
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test_cp = compiler.CompiledProgram(
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test_program, build_strategy=build_strategy
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)
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for i in range(5):
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_ = exe.run(train_cp, fetch_list=[loss], feed=feed_dict)
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(test_loss,) = exe.run(
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test_cp, fetch_list=[loss], feed=feed_dict
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)
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(train_loss,) = exe.run(
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train_cp, fetch_list=[loss], feed=feed_dict
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)
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avg_test_loss_val = np.array(test_loss).mean()
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if math.isnan(float(avg_test_loss_val)):
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sys.exit("got NaN loss, testing failed.")
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avg_train_loss_val = np.array(train_loss).mean()
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if math.isnan(float(avg_train_loss_val)):
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sys.exit("got NaN loss, training failed.")
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np.testing.assert_allclose(
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train_loss,
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test_loss,
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rtol=1e-05,
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atol=1e-08,
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err_msg='Train loss: '
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+ str(train_loss)
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+ '\n Test loss:'
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+ str(test_loss),
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)
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def test_parallel_testing_with_new_strategy(self):
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build_strategy = base.BuildStrategy()
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self.assertFalse(build_strategy.fuse_elewise_add_act_ops)
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build_strategy.fuse_elewise_add_act_ops = True
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# FIXME: currently fuse_elewise_add_act_ops not compatible with below options
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build_strategy.enable_inplace = False
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build_strategy.memory_optimize = False
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pass_builder = build_strategy._finalize_strategy_and_create_passes()
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self.assertTrue(
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"fuse_elewise_add_act_pass"
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in [p.type() for p in pass_builder.all_passes()]
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)
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origin_len = len(pass_builder.all_passes())
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viz_pass = pass_builder.append_pass("graph_viz_pass")
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self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
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pass_builder.insert_pass(
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len(pass_builder.all_passes()), "graph_viz_pass"
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)
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self.assertEqual(origin_len + 2, len(pass_builder.all_passes()))
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pass_builder.remove_pass(len(pass_builder.all_passes()) - 1)
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self.assertEqual(origin_len + 1, len(pass_builder.all_passes()))
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with (
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paddle.pir_utils.OldIrGuard(),
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tempfile.TemporaryDirectory(prefix="dot_path_") as tmpdir,
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):
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graph_viz_path = os.path.join(tmpdir, 'test_viz_pass.dot')
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viz_pass.set("graph_viz_path", graph_viz_path)
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self.check_network_convergence(
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use_cuda=(core.is_compiled_with_cuda() or is_custom_device()),
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build_strategy=build_strategy,
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)
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try:
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os.stat(graph_viz_path)
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except OSError:
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self.assertFalse(True)
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if __name__ == '__main__':
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unittest.main()
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