import unittest import paddle.v2.fluid.core as core import numpy import paddle.v2.fluid.layers as layers from paddle.v2.fluid.framework import Program, program_guard from paddle.v2.fluid.executor import Executor from paddle.v2.fluid.backward import append_backward_ops class TestCPULoDTensorArrayOps(unittest.TestCase): def place(self): return core.CPUPlace() def test_lod_tensor_to_array_level_0(self): tensor = core.LoDTensor() tensor.set( numpy.arange(10).reshape(10, 1).astype('int32'), self.place()) tensor.set_lod([[0, 3, 9, 10]]) expect = map(lambda x: numpy.array(x).astype('int32'), [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]]) self.main( tensor=tensor, expect_array=expect, expect_lod=[] * 6, expect_max_len=6) def test_lod_tensor_to_array_level_0_empty_seq(self): tensor = core.LoDTensor() tensor.set( numpy.arange(10).reshape(10, 1).astype('int32'), self.place()) tensor.set_lod([[0, 3, 9, 9, 10]]) expect = map(lambda x: numpy.array(x).astype('int32'), [[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]]) self.main( tensor=tensor, expect_array=expect, expect_lod=[] * 6, expect_max_len=6) def test_lod_tensor_to_array_level_1(self): tensor = core.LoDTensor() tensor.set( numpy.arange(20).reshape(20, 1).astype('int32'), self.place()) tensor.set_lod([[0, 2, 5], [0, 3, 9, 11, 17, 20]]) expect = [ numpy.array( [9, 10, 0, 1, 2], dtype='int32'), numpy.array( [11, 12, 13, 14, 15, 16, 3, 4, 5, 6, 7, 8], dtype='int32'), numpy.array( [17, 18, 19], dtype='int32') ] lod = [[[0, 2, 5]], [[0, 6, 12]], [[0, 3]]] self.main( tensor=tensor, expect_array=expect, expect_lod=lod, expect_max_len=3) def test_lod_tensor_to_array_level_1_empty_seq(self): tensor = core.LoDTensor() tensor.set( numpy.arange(31).reshape(31, 1).astype('int32'), self.place()) tensor.set_lod([[0, 3, 5, 9, 11], [0, 3, 7, 11, 11, 12, 17, 19, 21, 23, 30, 31]]) expect = [ numpy.array( item, dtype='int32') for item in [[ 12, 13, 14, 15, 16, 0, 1, 2, 23, 24, 25, 26, 27, 28, 29 ], [17, 18, 3, 4, 5, 6, 11, 30], [19, 20, 7, 8, 9, 10], [21, 22]] ] lod = [[[0, 5, 8, 8, 15]], [[0, 2, 6, 7, 8]], [[0, 2, 6]], [[0, 2]]] self.main( tensor=tensor, expect_array=expect, expect_lod=lod, expect_max_len=4) def test_lod_tensor_to_array_level_2(self): tensor = core.LoDTensor() tensor.set( numpy.arange(50).reshape(50, 1).astype('int32'), self.place()) tensor.set_lod([[0, 2, 5, 6], [0, 2, 5, 6, 10, 12, 13], [0, 3, 7, 11, 17, 21, 22, 23, 27, 31, 39, 45, 46, 50]]) expect = [ numpy.array( item, dtype='int32') for item in [[21, 0, 1, 2, 3, 4, 5, 6, 46, 47, 48, 49], range( 22, 39) + range(7, 21), range(39, 46)] ] lod = [[[0, 1, 3, 4], [0, 1, 4, 8, 12]], [[0, 4, 7], [0, 1, 5, 9, 17, 21, 27, 31]], [[0, 2], [0, 6, 7]]] self.main( tensor=tensor, expect_array=expect, expect_lod=lod, expect_max_len=3) def test_lod_tensor_to_array_level_2_skip_level(self): tensor = core.LoDTensor() tensor.set( numpy.arange(50).reshape(50, 1).astype('int32'), self.place()) tensor.set_lod([[0, 2, 5, 6], [0, 2, 5, 6, 10, 12, 13], [0, 3, 7, 11, 17, 21, 22, 23, 27, 31, 39, 45, 46, 50]]) self.main( tensor=tensor, expect_array=None, expect_lod=None, expect_max_len=4, level=1) def main(self, tensor, expect_array, expect_lod, expect_max_len, level=0): place = self.place() program = Program() with program_guard(program): x = layers.data(name='x', shape=[10]) x.persistable = True table = layers.lod_rank_table(x, level=level) max_len = layers.max_sequence_len(table) max_len.persistable = True array = layers.lod_tensor_to_array(x, table) array.persistable = True result = layers.array_to_lod_tensor(array, table) result.persistable = True exe = Executor(place) scope = core.Scope() exe.run(program, feed={'x': tensor}, scope=scope) var = scope.find_var(array.name) array = var.get_lod_tensor_array() if expect_array is not None and expect_lod is not None: self.check_array_same(array, expect_array, expect_lod) self.check_tensor_same(scope.find_var(result.name).get_tensor(), tensor) self.assertEqual( numpy.array(scope.find_var(max_len.name).get_tensor())[0], expect_max_len) def check_array_same(self, array, expect_tensor, expect_lod): self.assertEqual(len(expect_tensor), len(array)) for i, exp in enumerate(zip(expect_tensor, expect_lod)): exp_tensor, exp_lod = exp exp_tensor = numpy.expand_dims(exp_tensor, axis=1) self.assertTrue(numpy.allclose(exp_tensor, numpy.array(array[i]))) self.assertEqual(exp_lod, array[i].lod()) def check_tensor_same(self, actual, expect): self.assertTrue( numpy.allclose(numpy.array(actual), numpy.array(expect))) self.assertEqual(actual.lod(), expect.lod()) class TestCPULoDTensorArrayOpGrad(unittest.TestCase): def test_grad(self): place = core.CPUPlace() program = Program() with program_guard(program): x = layers.data( name='x', shape=[1], dtype='float32', stop_gradient=False) table = layers.lod_rank_table(x, level=0) array = layers.lod_tensor_to_array(x, table) result = layers.array_to_lod_tensor(array, table) mean = layers.mean(x=result) append_backward_ops(mean) tensor = core.LoDTensor() tensor.set(numpy.arange(10).reshape(10, 1).astype('float32'), place) tensor.set_lod([[0, 3, 9, 10]]) g_vars = program.global_block().var(x.name + "@GRAD") exe = Executor(place) g_out = [ numpy.array(item).sum() for item in exe.run(program, feed={'x': tensor}, fetch_list=[g_vars], return_numpy=False) ] g_out_sum = numpy.array(g_out).sum() self.assertAlmostEqual(1.0, g_out_sum, delta=0.1) if __name__ == '__main__': unittest.main()