wehub-resource-sync
249 lines
9.0 KiB
Python
249 lines
9.0 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 unittest
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import numpy
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from utils import compare_legacy_with_pt
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.base.backward import append_backward
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from paddle.base.executor import Executor
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from paddle.base.framework import in_pir_mode
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from paddle.incubate.layers.nn import shuffle_batch
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paddle.enable_static()
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class TestWhileOp(unittest.TestCase):
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def simple_net(self):
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d0 = paddle.static.data("d0", shape=[10], dtype='float32')
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d1 = paddle.static.data("d1", shape=[10], dtype='float32')
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d2 = paddle.static.data("d2", shape=[10], dtype='float32')
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d0.persistable = True
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d0.stop_gradient = False
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d1.persistable = True
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d2.persistable = True
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i = paddle.zeros(shape=[1], dtype='int64')
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i.stop_gradient = True
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i.persistable = True
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init = paddle.zeros(shape=[10], dtype='float32')
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mem_array = paddle.tensor.array_write(x=init, i=i)
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data_array = paddle.tensor.array_write(x=d0, i=i)
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mem_array.stop_gradient = False
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data_array.stop_gradient = False
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mem_array.persistable = True
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i = paddle.increment(i)
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paddle.tensor.array_write(d1, i, array=data_array)
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i = paddle.increment(i)
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paddle.tensor.array_write(d2, i, array=data_array)
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i = paddle.zeros(shape=[1], dtype='int64')
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i.stop_gradient = True
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array_len = paddle.tensor.fill_constant(
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shape=[1], dtype='int64', value=1
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)
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array_len.stop_gradient = True
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cond = paddle.less_than(x=i, y=array_len)
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j = paddle.tensor.fill_constant(shape=[1], dtype='int64', value=1)
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j.stop_gradient = True
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array_len2 = paddle.tensor.fill_constant(
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shape=[1], dtype='int64', value=3
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)
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array_len2.stop_gradient = True
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cond2 = paddle.less_than(x=j, y=array_len2)
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while_op = paddle.static.nn.control_flow.While(cond=cond)
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while_op2 = paddle.static.nn.control_flow.While(cond=cond2)
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with while_op.block():
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d = paddle.tensor.array_read(array=data_array, i=i)
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prev = paddle.tensor.array_read(array=mem_array, i=i)
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result = paddle.add_n([d, prev])
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i = paddle.increment(x=i)
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paddle.tensor.array_write(result, i=i, array=mem_array)
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with while_op2.block():
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d2 = paddle.tensor.array_read(array=data_array, i=j)
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prev2 = paddle.tensor.array_read(array=mem_array, i=j)
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result2 = paddle.add_n([d2, prev2])
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paddle.increment(x=j)
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paddle.tensor.array_write(result2, i=j, array=mem_array)
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paddle.assign(paddle.less_than(x=j, y=array_len2), cond2)
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paddle.assign(paddle.less_than(x=i, y=array_len), cond)
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sum_result = paddle.tensor.array_read(array=mem_array, i=j)
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loss = paddle.mean(sum_result)
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return loss, sum_result
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def test_simple_net(self):
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main_program = base.Program()
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startup_program = base.Program()
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with base.program_guard(main_program, startup_program):
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loss, sum_result = self.simple_net()
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append_backward(loss)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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d = []
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for i in range(3):
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d.append(numpy.random.random(size=[10]).astype('float32'))
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outs = exe.run(
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feed={'d0': d[0], 'd1': d[1], 'd2': d[2]},
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fetch_list=[sum_result],
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)
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self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
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def test_simple_net_forward(self):
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main_program = base.Program()
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startup_program = base.Program()
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with base.program_guard(main_program, startup_program):
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self.simple_net()
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if in_pir_mode():
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binary = main_program
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else:
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binary = base.compiler.CompiledProgram(main_program)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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d = []
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for i in range(3):
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d.append(numpy.random.random(size=[10]).astype('float32'))
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for _ in range(2):
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exe.run(binary, feed={'d0': d[0], 'd1': d[1], 'd2': d[2]})
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@compare_legacy_with_pt
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def test_exceptions(self):
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i = paddle.zeros(shape=[2], dtype='int64')
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array_len = paddle.tensor.fill_constant(
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shape=[2], dtype='int64', value=1
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)
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cond = paddle.less_than(x=i, y=array_len)
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with self.assertRaises(TypeError):
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paddle.static.nn.control_flow.While(cond=cond)
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cond = paddle.cast(cond, dtype='float64')
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with self.assertRaises(TypeError):
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paddle.static.nn.control_flow.While(cond=cond)
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class BadInputTest(unittest.TestCase):
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@compare_legacy_with_pt
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def test_error(self):
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with base.program_guard(base.Program()):
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def test_bad_x():
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x = [1, 2, 3]
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paddle.increment(x)
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self.assertRaises(TypeError, test_bad_x)
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class TestIgnoreVarNameInWhile(unittest.TestCase):
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def test_ignore_var(self):
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def cond(i, ten, temp, y):
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return i < ten
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def body_func(i, ten, batch_info, origin_seq):
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print(batch_info)
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batch_info = shuffle_batch(batch_info)
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print(batch_info)
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i = i + 1
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return [i, ten, batch_info, origin_seq]
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x = paddle.static.data(name='x', shape=[-1, 1, 4], dtype='float32')
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y = paddle.static.data(name='y', shape=[-1, 1, 1], dtype='float32')
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if not in_pir_mode():
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x.desc.set_need_check_feed(False)
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y.desc.set_need_check_feed(False)
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temp = paddle.concat([x, y], axis=-1)
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i = paddle.tensor.fill_constant(shape=[1], value=0, dtype='int32')
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num = paddle.tensor.fill_constant(shape=[1], value=5, dtype='int32')
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i, ten, shuffle_temp, y = paddle.static.nn.while_loop(
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cond, body_func, [i, num, temp, y]
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)
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output = shuffle_temp
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exe = base.Executor(base.CPUPlace())
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exe.run(base.default_startup_program())
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input_x = numpy.array(
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[[1.0, 2.0, 3.0, 4.0], [4.0, 5.0, 6.0, 7.0], [7.0, 8.0, 9.0, 10.0]]
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).astype('float32')
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input_x = input_x.reshape(3, 1, 4)
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input_y = numpy.array([[10.0], [12.0], [33.0]]).astype('float32')
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input_y = input_y.reshape(3, 1, 1)
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(res,) = exe.run(
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base.default_main_program(),
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feed={'x': input_x, 'y': input_y},
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fetch_list=[output],
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)
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self.assertListEqual(list(res.shape), [3, 1, 5])
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class TestOutputsMustExistsInputs(unittest.TestCase):
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@compare_legacy_with_pt
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def test_outputs_exists_inputs(self):
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"""
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We guarantee that the output tensor must be in the input tensor, so that the output and input can correspond to each other, but the input can be greater than the number of outputs. It's required in paddle2onnx.
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"""
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main_program = base.Program()
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startup_program = base.Program()
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with base.program_guard(main_program, startup_program):
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def func(x):
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s = paddle.zeros([])
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i = paddle.ones([])
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max_len = paddle.shape(x)
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def cond(i, s, x):
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return i < max_len
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def body(i, s, x):
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iter = x[i]
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s += iter
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i += 1
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return i, s, x
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[i, s, x] = paddle.static.nn.while_loop(cond, body, [i, s, x])
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return s
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paddle.enable_static()
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x = paddle.static.data(shape=[-1], name='x', dtype='float32')
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func(x)
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# NOTE(winter-wang): The while_op in pir mode doesn't need following constraint, so here only check when in non-pir mode.
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if not in_pir_mode():
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for op in main_program.block(0).ops:
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if op.type == "while":
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for out_name in op.output("Out"):
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if out_name in op.input("Condition"):
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continue
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self.assertTrue(
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out_name in op.input("X"),
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f"In while op, the variable in output(`Out`) must exists in inputs(`X`), but the variable with name `{out_name}` not meet the precondition.",
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)
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if __name__ == '__main__':
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unittest.main()
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