# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy import paddle import paddle.fluid as fluid import paddle.fluid.core as core import paddle.fluid.layers as layers from paddle.fluid.backward import append_backward from paddle.fluid.executor import Executor paddle.enable_static() class TestWhileOp(unittest.TestCase): def simple_net(self): d0 = paddle.static.data("d0", shape=[10], dtype='float32') d1 = paddle.static.data("d1", shape=[10], dtype='float32') d2 = paddle.static.data("d2", shape=[10], dtype='float32') i = layers.zeros(shape=[1], dtype='int64') i.stop_gradient = True init = layers.zeros(shape=[10], dtype='float32') mem_array = paddle.tensor.array_write(x=init, i=i) data_array = paddle.tensor.array_write(x=d0, i=i) i = paddle.increment(i) paddle.tensor.array_write(d1, i, array=data_array) i = paddle.increment(i) paddle.tensor.array_write(d2, i, array=data_array) i = layers.zeros(shape=[1], dtype='int64') i.stop_gradient = True array_len = layers.fill_constant(shape=[1], dtype='int64', value=1) array_len.stop_gradient = True cond = paddle.less_than(x=i, y=array_len) j = layers.fill_constant(shape=[1], dtype='int64', value=1) j.stop_gradient = True array_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3) array_len2.stop_gradient = True cond2 = paddle.less_than(x=j, y=array_len2) while_op = paddle.static.nn.control_flow.While(cond=cond) while_op2 = paddle.static.nn.control_flow.While(cond=cond2) with while_op.block(): d = paddle.tensor.array_read(array=data_array, i=i) prev = paddle.tensor.array_read(array=mem_array, i=i) result = paddle.add_n([d, prev]) i = paddle.increment(x=i) paddle.tensor.array_write(result, i=i, array=mem_array) paddle.assign(paddle.less_than(x=i, y=array_len), cond) with while_op2.block(): d2 = paddle.tensor.array_read(array=data_array, i=j) prev2 = paddle.tensor.array_read(array=mem_array, i=j) result2 = paddle.add_n([d2, prev2]) j = paddle.increment(x=j) paddle.tensor.array_write(result2, i=j, array=mem_array) paddle.assign(paddle.less_than(x=j, y=array_len2), cond2) sum_result = paddle.tensor.array_read(array=mem_array, i=j) loss = paddle.mean(sum_result) return loss, sum_result def test_simple_net(self): main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program, startup_program): loss, sum_result = self.simple_net() append_backward(loss) cpu = core.CPUPlace() exe = Executor(cpu) d = [] for i in range(3): d.append(numpy.random.random(size=[10]).astype('float32')) outs = exe.run( feed={'d0': d[0], 'd1': d[1], 'd2': d[2]}, fetch_list=[sum_result], ) self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01) def test_simple_net_forward(self): main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program, startup_program): self.simple_net() binary = fluid.compiler.CompiledProgram(main_program) cpu = core.CPUPlace() exe = Executor(cpu) d = [] for i in range(3): d.append(numpy.random.random(size=[10]).astype('float32')) for _ in range(2): exe.run(binary, feed={'d0': d[0], 'd1': d[1], 'd2': d[2]}) def test_exceptions(self): i = layers.zeros(shape=[2], dtype='int64') array_len = layers.fill_constant(shape=[2], dtype='int64', value=1) cond = paddle.less_than(x=i, y=array_len) with self.assertRaises(TypeError): paddle.static.nn.control_flow.While(cond=cond) cond = paddle.cast(cond, dtype='float64') with self.assertRaises(TypeError): paddle.static.nn.control_flow.While(cond=cond) class BadInputTest(unittest.TestCase): def test_error(self): with fluid.program_guard(fluid.Program()): def test_bad_x(): x = [1, 2, 3] paddle.increment(x) self.assertRaises(TypeError, test_bad_x) class TestIgnoreVarNameInWhile(unittest.TestCase): def test_ignore_var(self): def cond(i, ten, temp, y): return i < ten def body_func(i, ten, batch_info, origin_seq): print(batch_info) batch_info = fluid.contrib.layers.shuffle_batch(batch_info) print(batch_info) i = i + 1 return [i, ten, batch_info, origin_seq] x = paddle.static.data(name='x', shape=[-1, 1, 4], dtype='float32') y = paddle.static.data(name='y', shape=[-1, 1, 1], dtype='float32') x.desc.set_need_check_feed(False) y.desc.set_need_check_feed(False) temp = paddle.concat([x, y], axis=-1) i = layers.fill_constant(shape=[1], value=0, dtype='int32') num = layers.fill_constant(shape=[1], value=5, dtype='int32') i, ten, shuffle_temp, y = paddle.static.nn.while_loop( cond, body_func, [i, num, temp, y] ) output = shuffle_temp exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) input_x = numpy.array([[1, 2, 3, 4], [4, 5, 6, 7], [7, 8, 9, 10]]) input_x = input_x.reshape(3, 1, 4) input_y = numpy.array([[10], [12], [33]]) input_y = input_y.reshape(3, 1, 1) (res,) = exe.run( fluid.default_main_program(), feed={'x': input_x, 'y': input_y}, fetch_list=[output], ) self.assertListEqual(list(res.shape), [3, 1, 5]) class TestOutputsMustExistsInputs(unittest.TestCase): def test_outputs_exists_inputs(self): """ 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. """ main_program = fluid.Program() startup_program = fluid.Program() with fluid.program_guard(main_program, startup_program): def func(x): s = paddle.zeros([1]) i = paddle.ones([1]) max_len = paddle.shape(x)[0] def cond(i, s, x): return i < max_len def body(i, s, x): iter = x[i] s += iter i += 1 return i, s, x [i, s, x] = paddle.static.nn.while_loop(cond, body, [i, s, x]) return s paddle.enable_static() x = paddle.static.data(shape=[-1], name='x', dtype='float32') func(x) for op in main_program.block(0).ops: if op.type == "while": for out_name in op.output("Out"): if out_name in op.input("Condition"): continue self.assertTrue( out_name in op.input("X"), "In while op, the variable in output(`Out`) must exists in inputs(`X`), but the variable with name `{}` not meet the precondition.".format( out_name ), ) if __name__ == '__main__': unittest.main()