add IfElse test case for ir memory optimize (#15998)
* add ir memory optimize test case for IfElse op, test=develop * fix some unitttest failure by force using the python memory_optimize, test=develop * tweak comments, test=develop * fix unittest, test=develop * fix unittest, test=developrevert-16045-imperative_remove_desc
liuwei1031
6 years ago
committed by
ceci3
parent
503efa8b86
commit
9cc6f4009f
@ -0,0 +1,123 @@
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# 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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# nlp model stack of op operate on lod. It's a classical test case in optimize pass.
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from __future__ import print_function
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import unittest
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import paddle.fluid.core as core
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from paddle.fluid import compiler, Program, program_guard
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from paddle.fluid.executor import Executor
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from paddle.fluid.backward import append_backward
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from paddle.fluid.optimizer import MomentumOptimizer
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from ir_memory_optimize_net_base import TestIrMemOptBase
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class TestIrMemoryOptimizeIfElseOp(unittest.TestCase):
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def check_network_convergence(self, use_cuda=True, py_opt=False,
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iter_num=5):
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prog = Program()
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startup_prog = Program()
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prog.random_seed = 100
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startup_prog.random_seed = 100
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with program_guard(prog, startup_prog):
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image = layers.data(name='x', shape=[784], dtype='float32')
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label = layers.data(name='y', shape=[1], dtype='int64')
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limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
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cond = layers.less_than(x=label, y=limit)
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ie = layers.IfElse(cond)
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with ie.true_block():
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true_image = ie.input(image)
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hidden = layers.fc(input=true_image, size=100, act='tanh')
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prob = layers.fc(input=hidden, size=10, act='softmax')
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ie.output(prob)
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with ie.false_block():
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false_image = ie.input(image)
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hidden = layers.fc(input=false_image, size=200, act='tanh')
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prob = layers.fc(input=hidden, size=10, act='softmax')
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ie.output(prob)
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prob = ie()
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loss = layers.cross_entropy(input=prob[0], label=label)
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avg_loss = layers.mean(loss)
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optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
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optimizer.minimize(avg_loss, startup_prog)
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train_reader = paddle.batch(
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paddle.dataset.mnist.train(), batch_size=200)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = Executor(place)
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exec_strategy = fluid.ExecutionStrategy()
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exec_strategy.use_cuda = use_cuda
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if py_opt:
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fluid.memory_optimize(fluid.default_main_program())
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train_cp = compiler.CompiledProgram(fluid.default_main_program())
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train_cp = train_cp.with_data_parallel(
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loss_name=avg_loss.name, exec_strategy=exec_strategy)
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fetch_list = [avg_loss.name]
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exe.run(startup_prog)
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PASS_NUM = 100
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loop = 0
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ret = []
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for pass_id in range(PASS_NUM):
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for data in train_reader():
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x_data = np.array([x[0] for x in data]).astype("float32")
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y_data = np.array([x[1] for x in data]).astype("int64")
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y_data = y_data.reshape((y_data.shape[0], 1))
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outs = exe.run(train_cp,
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feed={'x': x_data,
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'y': y_data},
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fetch_list=[avg_loss])
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loop += 1
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ret.append(outs[0])
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if iter_num == loop:
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return ret
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return ret
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def test_ifelse(self):
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ret1 = self.check_network_convergence(False, True)
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print(ret1)
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ret2 = self.check_network_convergence(False, False)
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print(ret2)
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self.assertTrue(np.allclose(ret1, ret2))
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if fluid.core.is_compiled_with_cuda():
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ret1 = self.check_network_convergence(True, True)
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print(ret1)
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ret2 = self.check_network_convergence(True, False)
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print(ret2)
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self.assertTrue(np.allclose(ret1, ret2))
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#self.assertEqual(ret1, ret2)
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if __name__ == "__main__":
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unittest.main()
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