wehub-resource-sync
134 lines
5.0 KiB
Python
134 lines
5.0 KiB
Python
# Copyright (c) 2022 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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import paddle
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from paddle import base
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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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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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i = paddle.zeros(shape=[1], dtype='int64')
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i.stop_gradient = 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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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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paddle.assign(paddle.less_than(x=i, y=array_len), cond)
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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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j = 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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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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xpu_place = paddle.XPUPlace(0)
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exe = Executor(xpu_place)
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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 paddle.framework.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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xpu_place = paddle.XPUPlace(0)
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exe = Executor(xpu_place)
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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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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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if __name__ == '__main__':
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unittest.main()
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