[CodeStyle] remove crlf for python files (#46155)

c6c9c186 · Nyakku Shigure · GitHub · 9941ec12 · c6c9c186 · c6c9c186
8 changed file
--- a/python/paddle/fluid/tests/unittests/asp/test_asp_utils.py
+++ b/python/paddle/fluid/tests/unittests/asp/test_asp_utils.py
 # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 # Copyright (c) 2021 NVIDIA Corporation.  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.
 from __future__ import print_function
 import unittest
 import threading, time
 import paddle
 import numpy as np
 class TestASPUtils(unittest.TestCase):
    def test_get_check_method(self):
        self.assertEqual(
            paddle.fluid.contrib.sparsity.CheckMethod.get_checking_method(
                paddle.fluid.contrib.sparsity.MaskAlgo.MASK_1D),
            paddle.fluid.contrib.sparsity.CheckMethod.CHECK_1D)
        self.assertEqual(
            paddle.fluid.contrib.sparsity.CheckMethod.get_checking_method(
                paddle.fluid.contrib.sparsity.MaskAlgo.MASK_2D_GREEDY),
            paddle.fluid.contrib.sparsity.CheckMethod.CHECK_2D)
        self.assertEqual(
            paddle.fluid.contrib.sparsity.CheckMethod.get_checking_method(
                paddle.fluid.contrib.sparsity.MaskAlgo.MASK_2D_BEST),
            paddle.fluid.contrib.sparsity.CheckMethod.CHECK_2D)
    def test_density(self):
        x = np.array([[1.0, 1.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0],
                      [1.0, 0.0, 0.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0],
                      [0.0, 1.0, 0.0, 0.0, 1.0]])
        self.assertEqual(paddle.incubate.asp.calculate_density(x), 0.56)
        x[:, 0] = 0.0
        self.assertEqual(paddle.incubate.asp.calculate_density(x), 0.4)
    def test_check_mask_1d(self):
        x = np.array([[1.0, 0.0, 0.0, 1.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0],
                      [1.0, 1.0, 0.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 1.0],
                      [0.0, 1.0, 0.0, 0.0, 1.0]])
        self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_1d(x, 2, 4))
        self.assertFalse(paddle.fluid.contrib.sparsity.check_mask_1d(x, 3, 4))
        self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_1d(x, 2, 5))
        self.assertFalse(paddle.fluid.contrib.sparsity.check_mask_1d(x, 3, 5))
        self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_1d(x, 3, 6))
        self.assertFalse(paddle.fluid.contrib.sparsity.check_mask_1d(x, 4, 6))
    def test_get_mask_1d(self):
        for _ in range(10):
            x = np.random.randint(10, size=(5, 5))
            x = paddle.fluid.contrib.sparsity.get_mask_1d(x, 2, 4)
            self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_1d(
                x, 2, 4))
            x = np.random.randn(5, 4)
            x = paddle.fluid.contrib.sparsity.get_mask_1d(x, 2, 4)
            self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_1d(
                x, 2, 4))
    def test_check_mask_2d(self):
        x = np.array([[1.0, 0.0, 0.0, 1.0, 1.0], [0.0, 1.0, 0.0, 0.0, 0.0],
                      [0.0, 0.0, 1.0, 0.0, 1.0], [1.0, 1.0, 0.0, 0.0, 0.0],
                      [0.0, 1.0, 0.0, 0.0, 1.0]])
        self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_2d(x, 2, 4))
        self.assertFalse(paddle.fluid.contrib.sparsity.check_mask_2d(x, 3, 4))
        self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_2d(x, 2, 5))
        self.assertFalse(paddle.fluid.contrib.sparsity.check_mask_2d(x, 3, 5))
        self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_2d(x, 3, 6))
        self.assertFalse(paddle.fluid.contrib.sparsity.check_mask_2d(x, 4, 6))
    def test_get_mask_2d_greedy(self):
        for _ in range(10):
            x = np.random.randint(10, size=(5, 5))
            x = paddle.fluid.contrib.sparsity.get_mask_2d_greedy(x, 2, 4)
            self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_2d(
                x, 2, 4))
            x = np.random.randn(5, 4)
            x = paddle.fluid.contrib.sparsity.get_mask_2d_greedy(x, 2, 4)
            self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_2d(
                x, 2, 4))
    def test_get_mask_2d_best(self):
        for _ in range(10):
            x = np.random.randint(10, size=(5, 5))
            x = paddle.fluid.contrib.sparsity.get_mask_2d_best(x, 2, 4)
            self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_2d(
                x, 2, 4))
            x = np.random.randn(5, 4)
            x = paddle.fluid.contrib.sparsity.get_mask_2d_best(x, 2, 4)
            self.assertTrue(paddle.fluid.contrib.sparsity.check_mask_2d(
                x, 2, 4))
    def test_threadsafe_valid_2d_patterns(self):
        def get_reference(m=4, n=2):
            from itertools import permutations
            patterns = np.zeros(m)
            patterns[:n] = 1
            patterns = list(set(permutations(patterns.tolist())))
            patterns = patterns + patterns
            patterns = np.asarray(list(set(permutations(patterns, m))))
            valid = ((patterns.sum(axis=1) <= n).sum(
                axis=1) == m).nonzero()[0].reshape(-1)
            valid_patterns = np.empty((valid.shape[0], m, m))
            valid_patterns[:] = patterns[valid[:]]
            return valid_patterns
        for _ in range(4):
            computing_thread = threading.Thread(
                target=paddle.fluid.contrib.sparsity.utils.
                _compute_valid_2d_patterns,
                args=(2, 4))
            computing_thread.start()
        time.sleep(3)
        patterns_map = paddle.fluid.contrib.sparsity.utils._valid_2d_patterns
        reference_patterns = get_reference()
        reference_key = '4_2'
        self.assertTrue(reference_key in patterns_map)
        self.assertTrue(len(patterns_map) == 1)
        self.assertTrue(
            (reference_patterns == patterns_map[reference_key]).all())
    def test_check_sparsity(self):
        for _ in range(10):
            x = np.random.randint(10, size=(5))
            x_2d = x.reshape(1, x.shape[0])
            self.__test_1D_2D_sparsity_checking_methods(x_2d)
            x = np.random.randint(10, size=(5, 5))
            x_2d = x
            self.__test_1D_2D_sparsity_checking_methods(x_2d)
            x = np.random.randint(10, size=(5, 5, 5))
            x_2d = x.reshape(x.shape[0] * x.shape[1], x.shape[2])
            self.__test_1D_2D_sparsity_checking_methods(x_2d)
            x = np.random.randint(10, size=(5, 5, 5, 5))
            x_2d = x.reshape(x.shape[0], x.shape[1] * x.shape[2] * x.shape[3])
            self.__test_1D_2D_sparsity_checking_methods(x_2d)
    def test_create_mask(self):
        for _ in range(10):
            x = np.random.randint(10, size=(5))
            self.__test_1D_2D_sparse_mask_generation_methods(x)
            x = np.random.randint(10, size=(5, 5))
            self.__test_1D_2D_sparse_mask_generation_methods(x)
            x = np.random.randint(10, size=(5, 5, 5))
            self.__test_1D_2D_sparse_mask_generation_methods(x)
            x = np.random.randint(10, size=(5, 5, 5, 5))
            self.__test_1D_2D_sparse_mask_generation_methods(x)
    def __test_1D_2D_sparsity_checking_methods(self, x_2d):
        mask = paddle.fluid.contrib.sparsity.get_mask_1d(x_2d, 2, 4)
        self.assertEqual(
            paddle.fluid.contrib.sparsity.check_sparsity(
                mask,
                func_name=paddle.fluid.contrib.sparsity.CheckMethod.CHECK_1D,
                n=2,
                m=4), paddle.fluid.contrib.sparsity.check_mask_1d(mask, 2, 4))
        mask = paddle.fluid.contrib.sparsity.get_mask_2d_best(x_2d, 2, 4)
        self.assertEqual(
            paddle.fluid.contrib.sparsity.check_sparsity(
                mask,
                func_name=paddle.fluid.contrib.sparsity.CheckMethod.CHECK_2D,
                n=2,
                m=4), paddle.fluid.contrib.sparsity.check_mask_2d(mask, 2, 4))
    def __test_1D_2D_sparse_mask_generation_methods(self, x):
        mask = paddle.fluid.contrib.sparsity.create_mask(
            x,
            func_name=paddle.fluid.contrib.sparsity.MaskAlgo.MASK_1D,
            n=2,
            m=4)
        self.assertTrue(
            paddle.fluid.contrib.sparsity.check_sparsity(
                mask,
                func_name=paddle.fluid.contrib.sparsity.CheckMethod.CHECK_1D,
                n=2,
                m=4))
        mask = paddle.fluid.contrib.sparsity.create_mask(
            x,
            func_name=paddle.fluid.contrib.sparsity.MaskAlgo.MASK_2D_GREEDY,
            n=2,
            m=4)
        self.assertTrue(
            paddle.fluid.contrib.sparsity.check_sparsity(
                mask,
                func_name=paddle.fluid.contrib.sparsity.CheckMethod.CHECK_2D,
                n=2,
                m=4))
        mask = paddle.fluid.contrib.sparsity.create_mask(
            x,
            func_name=paddle.fluid.contrib.sparsity.MaskAlgo.MASK_2D_BEST,
            n=2,
            m=4)
        self.assertTrue(
            paddle.fluid.contrib.sparsity.check_sparsity(
                mask,
                func_name=paddle.fluid.contrib.sparsity.CheckMethod.CHECK_2D,
                n=2,
                m=4))
 if __name__ == '__main__':
    unittest.main()
--- a/python/paddle/fluid/tests/unittests/mkldnn/test_elementwise_sub_mkldnn_op.py
+++ b/python/paddle/fluid/tests/unittests/mkldnn/test_elementwise_sub_mkldnn_op.py
 #  Copyright (c) 2021 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.
 from __future__ import print_function
 import unittest
 import numpy as np
 from paddle import enable_static
 from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool, convert_float_to_uint16
 from paddle.fluid.framework import _current_expected_place
 import paddle.fluid.core as core
 @OpTestTool.skip_if(not (isinstance(_current_expected_place(), core.CPUPlace)),
                    "GPU is not supported")
 class TestMKLDNNElementwiseSubOp(OpTest):
    def setUp(self):
        self.op_type = "elementwise_sub"
        self.init_dtype()
        self.init_input_output()
        self.init_kernel_type()
        self.init_axis()
        self.inputs = {
            'X': OpTest.np_dtype_to_fluid_dtype(self.x),
            'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
        }
        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
        self.outputs = {'Out': self.out}
    def init_input_output(self):
        self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)
    def test_check_grad_normal(self):
        self.check_grad(['X', 'Y'], 'Out')
    def test_check_grad_ignore_x(self):
        self.check_grad(['Y'], 'Out', no_grad_set=set("X"))
    def test_check_grad_ignore_y(self):
        self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
    def init_axis(self):
        self.axis = -1
    def init_kernel_type(self):
        self.use_mkldnn = True
    def init_dtype(self):
        self.dtype = np.float32
    def test_check_output(self):
        self.check_output()
 class TestMKLDNNElementwiseSubOp2(TestMKLDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.random((100, )).astype(self.dtype)
        self.y = np.random.random((100, )).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)
 class TestMKLDNNElementwiseSubOp3(TestMKLDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
        self.y = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)
 class TestMKLDNNElementwiseSubOp4(TestMKLDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(1, 2, [2, 3, 4, 32]).astype(self.dtype)
        self.y = np.random.uniform(1, 2, [4, 32]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)
 class TestMKLDNNElementwiseSubOp5(TestMKLDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
        self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)
 class TestMKLDNNElementwiseSubOp_broadcast(TestMKLDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype)
        self.y = np.random.rand(10, 12).astype(self.dtype)
        self.out = self.x - self.y.reshape(1, 10, 12, 1)
    def init_axis(self):
        self.axis = 1
 class TestElementwiseSubOp_xsize_lessthan_ysize_sub(TestMKLDNNElementwiseSubOp):
    def init_input_output(self):
        self.x = np.random.rand(10, 12).astype(self.dtype)
        self.y = np.random.rand(2, 2, 10, 12).astype(self.dtype)
        self.out = self.x - self.y
    def init_axis(self):
        self.axis = 2
    def test_check_grad_normal(self):
        pass
    def test_check_grad_ignore_y(self):
        pass
    def test_check_grad_ignore_x(self):
        pass
 @OpTestTool.skip_if_not_cpu_bf16()
 class TestBf16(TestMKLDNNElementwiseSubOp):
    def setUp(self):
        self.op_type = "elementwise_sub"
        self.init_dtype()
        self.init_input_output()
        self.init_kernel_type()
        self.init_axis()
        self.x_bf16 = convert_float_to_uint16(self.x)
        self.y_bf16 = convert_float_to_uint16(self.y)
        self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
        self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
        self.outputs = {'Out': convert_float_to_uint16(self.out)}
    def init_dtype(self):
        self.dtype = np.float32
        self.mkldnn_data_type = "bfloat16"
    def init_input_output(self):
        self.x = np.random.random(100, ).astype(self.dtype)
        self.y = np.random.random(100, ).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)
    def test_check_output(self):
        self.check_output_with_place(core.CPUPlace())
    def test_check_grad_normal(self):
        self.check_grad_with_place(core.CPUPlace(), ["X", "Y"],
                                   "Out",
                                   user_defined_grads=[self.x, -self.x],
                                   user_defined_grad_outputs=[self.x_bf16])
    def test_check_grad_ignore_x(self):
        self.check_grad_with_place(core.CPUPlace(), ["Y"],
                                   "Out",
                                   user_defined_grads=[-self.y],
                                   user_defined_grad_outputs=[self.y_bf16])
    def test_check_grad_ignore_y(self):
        self.check_grad_with_place(core.CPUPlace(), ["X"],
                                   "Out",
                                   user_defined_grads=[self.x],
                                   user_defined_grad_outputs=[self.x_bf16])
 class TestBf16Broadcasting(TestBf16):
    def init_input_output(self):
        self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
        self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
        self.out = np.subtract(self.x, self.y)
    def compute_reduced_gradients(self, out_grads):
        part_sum = np.add.reduceat(out_grads, [0], axis=0)
        part_sum = np.add.reduceat(part_sum, [0], axis=1)
        part_sum = np.add.reduceat(part_sum, [0], axis=2)
        return -part_sum.flatten()
    def test_check_grad_normal(self):
        self.check_grad_with_place(
            core.CPUPlace(), ["X", "Y"],
            "Out",
            user_defined_grads=[self.x,
                                self.compute_reduced_gradients(self.x)],
            user_defined_grad_outputs=[self.x_bf16])
    def test_check_grad_ignore_x(self):
        self.check_grad_with_place(
            core.CPUPlace(), ["Y"],
            "Out",
            user_defined_grads=[self.compute_reduced_gradients(self.x)],
            user_defined_grad_outputs=[self.x_bf16])
 class TestInt8(TestMKLDNNElementwiseSubOp):
    def init_kernel_type(self):
        self.use_mkldnn = True
        self._cpu_only = True
    def init_dtype(self):
        self.dtype = np.int8
    def init_input_output(self):
        self.x = np.random.randint(0, 3, (12, 9)).astype("int8")
        self.y = np.random.randint(0, 3, (12, 9)).astype("int8")
        self.out = np.subtract(self.x, self.y)
    def init_scales(self):
        self.attrs['Scale_x'] = 1.0
        self.attrs['Scale_y'] = 1.0
        self.attrs['Scale_out'] = 1.0
    def test_check_output(self):
        self.init_scales()
        self.check_output()
    def test_check_grad_normal(self):
        pass
    def test_check_grad_ignore_x(self):
        pass
    def test_check_grad_ignore_y(self):
        pass
 if __name__ == '__main__':
    enable_static()
    unittest.main()
--- a/python/paddle/fluid/tests/unittests/mkldnn/test_fill_constant_mkldnn_op.py
+++ b/python/paddle/fluid/tests/unittests/mkldnn/test_fill_constant_mkldnn_op.py
 #   Copyright (c) 2022 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.
 from __future__ import print_function
 import unittest
 import numpy as np
 from paddle.fluid.tests.unittests.op_test import OpTest, OpTestTool
 import paddle
 @OpTestTool.skip_if_not_cpu_bf16()
 class TestFillConstant2DOneDNNOp(OpTest):
    def setUp(self):
        self.op_type = "fill_constant"
        self.dtype = np.float32
        self.shape_tensor_list = None
        self.shape_tensor = None
        self.str_value = ""
        real_shape = []
        self.value = 0.1
        self.set_inputs()
        self.set_attrs()
        if 'value' in self.attrs:
            self.value = self.attrs['value']
        if self.str_value != "":
            self.value = float(self.str_value)
        if 'ValueTensor' in self.inputs:
            self.value = self.inputs['ValueTensor']
        if 'shape' in self.attrs:
            real_shape = self.attrs['shape']
        if 'ShapeTensor' in self.inputs:
            real_shape = list(self.inputs['ShapeTensor'])
        if 'ShapeTensorList' in self.inputs:
            real_shape = []
            for shape_tensor in self.inputs['ShapeTensorList']:
                real_shape.append(shape_tensor[1].item())
        self.outputs = {'Out': np.full(real_shape, self.value)}
    def set_inputs(self):
        self.inputs = {}
    def set_attrs(self):
        self.attrs = {'shape': (3, 5), 'use_mkldnn': True, 'value': self.value}
    def test_check_output(self):
        self.check_output()
 class TestFillZerosLike4DShapeTensorPriorityOneDNNOp(TestFillConstant2DOneDNNOp
                                                     ):
    def set_inputs(self):
        self.inputs = {'ShapeTensor': np.array([5, 6, 7, 8]).astype("int32")}
 class TestFillZerosLike4DShapeTensorListPriorityOneDNNOp(
        TestFillConstant2DOneDNNOp):
    def set_inputs(self):
        shape = (4, 5, 6, 7)
        self.shape_tensor_list = []
        for index, elem in enumerate(shape):
            self.shape_tensor_list.append(("x" + str(index), np.ones(
                (1)).astype('int32') * elem))
        self.inputs = {'ShapeTensorList': self.shape_tensor_list}
 class TestFillZerosLike2DStringValueInfOneDNNOp(TestFillConstant2DOneDNNOp):
    def set_attrs(self):
        self.str_value = "inf"
        self.attrs = {'shape': (10, 13), 'use_mkldnn': True, 'str_value': "inf"}
 class TestFillZerosLike2DStringValueMinusInfOneDNNOp(TestFillConstant2DOneDNNOp
                                                     ):
    def set_attrs(self):
        self.str_value = "-inf"
        self.attrs = {
            'shape': (10, 13),
            'use_mkldnn': True,
            'str_value': "-inf"
        }
 class TestFillZerosLike2DStringValueFloatOneDNNOp(TestFillConstant2DOneDNNOp):
    def set_attrs(self):
        self.str_value = "0.123"
        self.attrs = {
            'shape': (10, 13),
            'use_mkldnn': True,
            'str_value': "0.123"
        }
 class TestFillZerosLike2DValueTensorPriorityOneDNNOp(
        TestFillZerosLike2DStringValueFloatOneDNNOp):
    def set_inputs(self):
        self.inputs = {'ValueTensor': np.atleast_1d(2.25).astype("float32")}
 if __name__ == "__main__":
    paddle.enable_static()
    unittest.main()
--- a/python/paddle/fluid/tests/unittests/sequence/test_sequence_first_step.py
+++ b/python/paddle/fluid/tests/unittests/sequence/test_sequence_first_step.py
 # 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 paddle.fluid as fluid
 from paddle.fluid.framework import convert_np_dtype_to_dtype_, Program, program_guard
 import paddle.fluid.core as core
 import numpy as np
 import copy
 import unittest
 import sys
 sys.path.append("../")
 from op_test import OpTest
 class TestSequenceFirstStepOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            def test_Variable():
                # the input must be Variable
                input_data = np.random.randint(1, 5, [4]).astype("int64")
                fluid.layers.sequence_last_step(input_data)
            self.assertRaises(TypeError, test_Variable)
            def test_input_dtype():
                # the dtype of input must be int64
                type_data = fluid.layers.data(name='type_data',
                                              shape=[7, 1],
                                              append_batch_size=False,
                                              dtype='int64',
                                              lod_level=1)
                fluid.layers.sequence_last_step(type_data)
            self.assertRaises(TypeError, test_input_dtype)
 if __name__ == '__main__':
    unittest.main()
--- a/python/paddle/fluid/tests/unittests/sequence/test_sequence_last_step.py
+++ b/python/paddle/fluid/tests/unittests/sequence/test_sequence_last_step.py
 # 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 paddle.fluid as fluid
 from paddle.fluid.framework import convert_np_dtype_to_dtype_, Program, program_guard
 import paddle.fluid.core as core
 import numpy as np
 import copy
 import unittest
 import sys
 sys.path.append("../")
 from op_test import OpTest
 class TestSequenceLastStepOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            def test_Variable():
                # the input must be Variable
                input_data = np.random.randint(1, 5, [4]).astype("int64")
                fluid.layers.sequence_last_step(input_data)
            self.assertRaises(TypeError, test_Variable)
            def test_input_dtype():
                # the dtype of input must be int64
                type_data = fluid.layers.data(name='type_data',
                                              shape=[7, 1],
                                              append_batch_size=False,
                                              dtype='int64',
                                              lod_level=1)
                fluid.layers.sequence_last_step(type_data)
            self.assertRaises(TypeError, test_input_dtype)
 if __name__ == '__main__':
    unittest.main()
--- a/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py
+++ b/python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py
 #   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.
 from __future__ import print_function
 import unittest
 import numpy as np
 import math
 import paddle.fluid.core as core
 from op_test import OpTest
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import random
 random.seed(2)
 np.set_printoptions(threshold=np.inf)
 paddle.enable_static()
 SIGMOID_THRESHOLD_MIN = -40.0
 SIGMOID_THRESHOLD_MAX = 13.0
 EXP_MAX_INPUT = 40.0
 class RandomWeight:
    def __init__(self):
        pass
    def updata_weight(self, hidden_size, input_size, dtype):
        std = 1.0 / math.sqrt(hidden_size)
        self.hidden_size = hidden_size
        self.input_size = input_size
        self.dtype = dtype
        self.weight_ih = np.random.uniform(low=-std,
                                           high=std,
                                           size=(4 * self.hidden_size,
                                                 self.input_size)).astype(dtype)
        self.weight_hh = np.random.uniform(
            low=-std, high=std,
            size=(4 * self.hidden_size, self.hidden_size)).astype(dtype)
        self.bias_ih = np.random.uniform(low=-std,
                                         high=std,
                                         size=(4 *
                                               self.hidden_size)).astype(dtype)
        self.bias_hh = np.random.uniform(low=-std,
                                         high=std,
                                         size=(4 *
                                               self.hidden_size)).astype(dtype)
 weight = RandomWeight()
 class LayerMixin(object):
    def __call__(self, *args, **kwargs):
        return self.forward(*args, **kwargs)
 class LayerListMixin(LayerMixin):
    def __init__(self, layers=None):
        self._layers = list(layers) if layers else []
    def append(self, layer):
        self._layers.append(layer)
    def __iter__(self):
        return iter(self._layers)
 class LSTMCell(LayerMixin):
    def __init__(self, input_size, hidden_size, bias=True):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.bias = bias
        self.dtype = np.float64
        self.parameters = dict()
        self.weight_ih = weight.weight_ih
        self.weight_hh = weight.weight_hh
        self.parameters['weight_ih'] = self.weight_ih
        self.parameters['weight_hh'] = self.weight_hh
        if bias:
            self.bias_ih = weight.bias_ih
            self.bias_hh = weight.bias_hh
            self.parameters['bias_ih'] = self.bias_ih
            self.parameters['bias_hh'] = self.bias_hh
        else:
            self.bias_ih = None
            self.bias_hh = None
    def init_state(self, inputs):
        batch_size = inputs.shape[0]
        init_h = np.zeros((batch_size, self.hidden_size), dtype=inputs.dtype)
        init_c = np.zeros((batch_size, self.hidden_size), dtype=inputs.dtype)
        return init_h, init_c
    def forward(self, inputs, hx=None):
        if hx is None:
            hx = self.init_state(inputs)
        pre_hidden, pre_cell = hx
        gates = np.matmul(inputs, self.weight_ih.T)
        if self.bias_ih is not None:
            gates = gates + self.bias_ih
        gates += np.matmul(pre_hidden, self.weight_hh.T)
        if self.bias_hh is not None:
            gates = gates + self.bias_hh
        chunked_gates = np.split(gates, 4, -1)
        i = 1.0 / (1.0 + np.exp(-chunked_gates[0]))
        f = 1.0 / (1.0 + np.exp(-chunked_gates[1]))
        o = 1.0 / (1.0 + np.exp(-chunked_gates[3]))
        c = f * pre_cell + i * np.tanh(chunked_gates[2])
        h = o * np.tanh(c)
        return h, (h, c)
 def sequence_mask(lengths, max_len=None):
    if max_len is None:
        max_len = np.max(lengths)
    else:
        assert max_len >= np.max(lengths)
    return np.arange(max_len) < np.expand_dims(lengths, -1)
 def update_state(mask, new, old):
    if not isinstance(old, (tuple, list)):
        return np.where(mask, new, old)
    else:
        return tuple(map(lambda x, y: np.where(mask, x, y), new, old))
 def rnn(cell,
        inputs,
        initial_states,
        sequence_length=None,
        time_major=False,
        is_reverse=False):
    if not time_major:
        inputs = np.transpose(inputs, [1, 0, 2])
    if is_reverse:
        inputs = np.flip(inputs, 0)
    if sequence_length is None:
        mask = None
    else:
        mask = np.transpose(sequence_mask(sequence_length), [1, 0])
        mask = np.expand_dims(mask, -1)
        if is_reverse:
            mask = np.flip(mask, 0)
    time_steps = inputs.shape[0]
    state = initial_states
    outputs = []
    for t in range(time_steps):
        x_t = inputs[t]
        if mask is not None:
            m_t = mask[t]
            y, new_state = cell(x_t, state)
            y = np.where(m_t, y, 0.)
            outputs.append(y)
            state = update_state(m_t, new_state, state)
        else:
            y, new_state = cell(x_t, state)
            outputs.append(y)
            state = new_state
    outputs = np.stack(outputs)
    final_state = state
    if is_reverse:
        outputs = np.flip(outputs, 0)
    if not time_major:
        outputs = np.transpose(outputs, [1, 0, 2])
    return outputs, final_state
 def birnn(cell_fw,
          cell_bw,
          inputs,
          initial_states,
          sequence_length=None,
          time_major=False):
    states_fw, states_bw = initial_states
    outputs_fw, states_fw = rnn(cell_fw,
                                inputs,
                                states_fw,
                                sequence_length,
                                time_major=time_major)
    outputs_bw, states_bw = rnn(cell_bw,
                                inputs,
                                states_bw,
                                sequence_length,
                                time_major=time_major,
                                is_reverse=True)
    outputs = np.concatenate((outputs_fw, outputs_bw), -1)
    final_states = (states_fw, states_bw)
    return outputs, final_states
 def flatten(nested):
    return list(_flatten(nested))
 def _flatten(nested):
    for item in nested:
        if isinstance(item, (list, tuple)):
            for subitem in _flatten(item):
                yield subitem
        else:
            yield item
 def unstack(array, axis=0):
    num = array.shape[axis]
    sub_arrays = np.split(array, num, axis)
    return [np.squeeze(sub_array, axis) for sub_array in sub_arrays]
 def dropout(array, p=0.0):
    if p == 0.0:
        return array
    mask = (np.random.uniform(size=array.shape) < (1 - p)).astype(array.dtype)
    return array * (mask / (1 - p))
 def split_states(states, bidirectional=False, state_components=1):
    if state_components == 1:
        states = unstack(states)
        if not bidirectional:
            return states
        else:
            return list(zip(states[::2], states[1::2]))
    else:
        assert len(states) == state_components
        states = tuple([unstack(item) for item in states])
        if not bidirectional:
            return list(zip(*states))
        else:
            states = list(zip(*states))
            return list(zip(states[::2], states[1::2]))
 def concat_states(states, bidirectional=False, state_components=1):
    if state_components == 1:
        return np.stack(flatten(states))
    else:
        states = flatten(states)
        componnets = []
        for i in range(state_components):
            componnets.append(states[i::state_components])
        return [np.stack(item) for item in componnets]
 class RNN(LayerMixin):
    def __init__(self, cell, is_reverse=False, time_major=False):
        super(RNN, self).__init__()
        self.cell = cell
        if not hasattr(self.cell, "call"):
            # for non-dygraph mode, `rnn` api uses cell.call
            self.cell.call = self.cell.forward
        self.is_reverse = is_reverse
        self.time_major = time_major
    def forward(self, inputs, initial_states=None, sequence_length=None):
        final_outputs, final_states = rnn(self.cell,
                                          inputs,
                                          initial_states=initial_states,
                                          sequence_length=sequence_length,
                                          time_major=self.time_major,
                                          is_reverse=self.is_reverse)
        return final_outputs, final_states
 class BiRNN(LayerMixin):
    def __init__(self, cell_fw, cell_bw, time_major=False):
        super(BiRNN, self).__init__()
        self.cell_fw = cell_fw
        self.cell_bw = cell_bw
        self.time_major = time_major
    def forward(self,
                inputs,
                initial_states=None,
                sequence_length=None,
                **kwargs):
        if isinstance(initial_states, (list, tuple)):
            assert len(initial_states) == 2, \
                "length of initial_states should be 2 when it is a list/tuple"
        else:
            initial_states = [initial_states, initial_states]
        outputs, final_states = birnn(self.cell_fw, self.cell_bw, inputs,
                                      initial_states, sequence_length,
                                      self.time_major)
        return outputs, final_states
 class RNNMixin(LayerListMixin):
    def forward(self, inputs, initial_states=None, sequence_length=None):
        batch_index = 1 if self.time_major else 0
        batch_size = inputs.shape[batch_index]
        dtype = inputs.dtype
        if initial_states is None:
            state_shape = (self.num_layers * self.num_directions, batch_size,
                           self.hidden_size)
            if self.state_components == 1:
                initial_states = np.zeros(state_shape, dtype)
            else:
                initial_states = tuple([
                    np.zeros(state_shape, dtype)
                    for _ in range(self.state_components)
                ])
        states = split_states(initial_states, self.num_directions == 2,
                              self.state_components)
        final_states = []
        for i, rnn_layer in enumerate(self):
            if i > 0:
                inputs = dropout(inputs, self.dropout)
            outputs, final_state = rnn_layer(inputs, states[i], sequence_length)
            final_states.append(final_state)
            inputs = outputs
        final_states = concat_states(final_states, self.num_directions == 2,
                                     self.state_components)
        return outputs, final_states
 class LSTM(RNNMixin):
    def __init__(self,
                 input_size,
                 hidden_size,
                 num_layers=1,
                 direction="forward",
                 dropout=0.,
                 time_major=False):
        super(LSTM, self).__init__()
        if direction in ["forward", "backward"]:
            is_reverse = direction == "backward"
            cell = LSTMCell(input_size, hidden_size)
            self.append(RNN(cell, is_reverse, time_major))
            for i in range(1, num_layers):
                cell = LSTMCell(hidden_size, hidden_size)
                self.append(RNN(cell, is_reverse, time_major))
        elif direction == "bidirectional":
            cell_fw = LSTMCell(input_size, hidden_size)
            cell_bw = LSTMCell(input_size, hidden_size)
            self.append(BiRNN(cell_fw, cell_bw, time_major))
            for i in range(1, num_layers):
                cell_fw = LSTMCell(2 * hidden_size, hidden_size)
                cell_bw = LSTMCell(2 * hidden_size, hidden_size)
                self.append(BiRNN(cell_fw, cell_bw, time_major))
        else:
            raise ValueError(
                "direction should be forward, backward or bidirectional, "
                "received direction = {}".format(direction))
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.dropout = dropout
        self.num_directions = 2 if direction == "bidirectional" else 1
        self.time_major = time_major
        self.num_layers = num_layers
        self.state_components = 2
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
 class TestCUDNNLstmOp(OpTest):
    def get_weight_names(self):
        weight_names = []
        for i in range(2 * self.num_layers):
            weight_names.append('weight{}'.format(i))
        for i in range(2 * self.num_layers):
            weight_names.append('bias{}'.format(i))
        return weight_names
    def setUp(self):
        self.op_type = "cudnn_lstm"
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
        self.sequence_length = None if core.is_compiled_with_rocm(
        ) else np.array([12, 11, 10, 9, 8], dtype=np.int32)
        self.num_layers = 1
        self.set_attrs()
        seq_length = 12
        batch_size = 5
        input_size = 21
        hidden_size = 21
        input = np.random.uniform(low=-0.1,
                                  high=0.1,
                                  size=(seq_length, batch_size,
                                        input_size)).astype(self.dtype)
        input[11][1:][:] = 0
        input[10][2:][:] = 0
        input[9][3:][:] = 0
        input[8][4:][:] = 0
        weight.updata_weight(hidden_size, input_size, self.dtype)
        rnn1 = LSTM(input_size,
                    hidden_size,
                    num_layers=self.num_layers,
                    time_major=True,
                    direction="forward")
        output, (last_hidden,
                 last_cell) = rnn1(input, sequence_length=self.sequence_length)
        flat_w = []
        num = 0
        for i in range(self.num_layers):
            if i == 0:
                weight_ih = weight.weight_ih
            else:
                weight_ih = weight.weight_hh
            flat_w.append(("weight" + str(num), weight_ih))
            num += 1
        for i in range(self.num_layers):
            weight_hh = weight.weight_hh
            flat_w.append(("weight" + str(num), weight_hh))
            num += 1
        num = 0
        for i in range(self.num_layers):
            bias_ih = weight.bias_ih
            flat_w.append(("bias" + str(num), bias_ih))
            num += 1
        for i in range(self.num_layers):
            bias_hh = weight.bias_hh
            flat_w.append(("bias" + str(num), bias_hh))
            num += 1
        init_h = np.zeros(
            (self.num_layers, batch_size, hidden_size)).astype(self.dtype)
        init_c = np.zeros(
            (self.num_layers, batch_size, hidden_size)).astype(self.dtype)
        state_out = np.ndarray((300)).astype("uint8")
        if core.is_compiled_with_rocm():
            for i in range(len(flat_w)):
                w = np.split(flat_w[i][1], 4, 0)
                w = [w[0], w[1], w[3], w[2]]
                w = np.concatenate(w)
                flat_w[i] = (flat_w[i][0], w)
        self.inputs = {
            'Input': input,
            'WeightList': flat_w,
            'InitH': init_h,
            'InitC': init_c,
            'SequenceLength': self.sequence_length
        }
        if self.sequence_length is None:
            self.inputs = {
                'Input': input,
                'WeightList': flat_w,
                'InitH': init_h,
                'InitC': init_c,
            }
        self.attrs = {
            'dropout_prob': 0.0,
            'is_bidirec': False,
            'input_size': input_size,
            'hidden_size': hidden_size,
            'num_layers': self.num_layers,
        }
        self.outputs = {
            'Out': output,
            "LastH": last_hidden,
            'LastC': last_cell,
            'Reserve': np.ndarray((400)).astype("uint8"),
            'StateOut': state_out
        }
    def set_attrs(self):
        pass
    def test_output_with_place(self):
        place = core.CUDAPlace(0)
        if core.is_compiled_with_rocm():
            self.check_output_with_place(place,
                                         atol=1e-5,
                                         no_check_set=['Reserve', 'StateOut'])
        else:
            self.check_output_with_place(place,
                                         no_check_set=['Reserve', 'StateOut'])
    def test_grad_with_place(self):
        place = core.CUDAPlace(0)
        var_name_list = self.get_weight_names()
        for var_name in var_name_list:
            self.check_grad_with_place(
                place, set(['Input', var_name, 'InitH', 'InitC']),
                ['Out', 'LastH', 'LastC'])
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
 class TestCUDNNlstmAPI(unittest.TestCase):
    def test_lstm(self):
        seq_len = 20
        batch_size = 5
        hidden_size = 20
        dropout_prob = 0.0
        num_layers = 1
        dtype = 'float32' if core.is_compiled_with_rocm() else 'float64'
        input = fluid.data(name='input',
                           shape=[seq_len, batch_size, hidden_size],
                           dtype=dtype)
        init_h = layers.fill_constant([num_layers, batch_size, hidden_size],
                                      dtype, 0.0)
        init_c = layers.fill_constant([num_layers, batch_size, hidden_size],
                                      dtype, 0.0)
        rnn_out, last_h, last_c = layers.lstm(input, init_h, init_c, seq_len,
                                              hidden_size, num_layers,
                                              dropout_prob, False)
        exe = fluid.Executor(fluid.CUDAPlace(0))
        exe.run(fluid.default_startup_program())
        input_i = np.random.uniform(low=-0.1,
                                    high=0.1,
                                    size=(seq_len, batch_size,
                                          hidden_size)).astype("float64")
        out = exe.run(fluid.default_main_program(),
                      feed={'input': input_i},
                      fetch_list=[rnn_out, last_h, last_c, 'cudnn_lstm_0.w_0'])
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                 "core is not compiled with CUDA")
 class TestCUDNNlstmAPI(unittest.TestCase):
    def test_lstm(self):
        seq_len = 20
        batch_size = 5
        hidden_size = 20
        dropout_prob = 0.0
        num_layers = 2
        dtype = 'float32' if core.is_compiled_with_rocm() else 'float64'
        input = fluid.data(name='input',
                           shape=[seq_len, batch_size, hidden_size],
                           dtype=dtype)
        init_h = layers.fill_constant([num_layers, batch_size, hidden_size],
                                      dtype, 0.0)
        init_c = layers.fill_constant([num_layers, batch_size, hidden_size],
                                      dtype, 0.0)
        rnn_out, last_h, last_c = layers.lstm(input, init_h, init_c, seq_len,
                                              hidden_size, num_layers,
                                              dropout_prob, False, True)
        exe = fluid.Executor(fluid.CUDAPlace(0))
        exe.run(fluid.default_startup_program())
        input_i = np.random.uniform(low=-0.1,
                                    high=0.1,
                                    size=(seq_len, batch_size,
                                          hidden_size)).astype(dtype)
        out = exe.run(fluid.default_main_program(),
                      feed={'input': input_i},
                      fetch_list=[rnn_out, last_h, last_c, 'cudnn_lstm_0.w_0'])
 if __name__ == '__main__':
    unittest.main()
--- a/python/paddle/fluid/tests/unittests/test_rnn_op.py
+++ b/python/paddle/fluid/tests/unittests/test_rnn_op.py
 #   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.
 from __future__ import print_function
 import unittest
 import numpy as np
 import math
 import paddle.fluid.core as core
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import random
 import sys
 from op_test import OpTest
 sys.path.append("./rnn")
 from rnn_numpy import SimpleRNN, LSTM, GRU
 from convert import get_params_for_net
 random.seed(2)
 np.set_printoptions(threshold=np.inf)
 paddle.enable_static()
 class TestRNNOp(OpTest):
    def get_weight_names(self):
        weight_names = []
        for i in range(self.num_layers):
            for j in range(0, 2 * self.direction_num):
                weight_names.append("{}.weight_{}".format(i, j))
        for i in range(self.num_layers):
            for j in range(0, 2 * self.direction_num):
                weight_names.append("{}.bias_{}".format(i, j))
        return weight_names
    def setUp(self):
        self.op_type = "rnn"
        self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
        self.sequence_length = None if core.is_compiled_with_rocm(
        ) else np.array([12, 11, 10, 9, 8], dtype=np.int32)
        self.num_layers = 1
        self.is_bidirec = False
        self.mode = "LSTM"
        self.is_test = False
        self.dropout = 0.0
        self.set_attrs()
        self.direction_num = 2 if self.is_bidirec else 1
        direction = "bidirectional" if self.is_bidirec else "forward"
        seq_length = 12
        batch_size = 5
        input_size = 3
        hidden_size = 2
        input = np.random.uniform(low=-0.1,
                                  high=0.1,
                                  size=(seq_length, batch_size,
                                        input_size)).astype(self.dtype)
        if self.sequence_length is not None:
            input[11][1:][:] = 0
            input[10][2:][:] = 0
            input[9][3:][:] = 0
            input[8][4:][:] = 0
        rnn1 = LSTM(input_size,
                    hidden_size,
                    num_layers=self.num_layers,
                    time_major=True,
                    direction=direction,
                    dropout=self.dropout,
                    dtype=self.dtype)
        flat_w = get_params_for_net(rnn1)
        output, (last_hidden,
                 last_cell) = rnn1(input, sequence_length=self.sequence_length)
        if core.is_compiled_with_rocm():
            def rocm_rnn_get_place():
                places = [core.CUDAPlace(0)]
                return places
            self._get_places = rocm_rnn_get_place
        init_h = np.zeros((self.num_layers * self.direction_num, batch_size,
                           hidden_size)).astype(self.dtype)
        init_c = np.zeros((self.num_layers * self.direction_num, batch_size,
                           hidden_size)).astype(self.dtype)
        state_out = np.ndarray((300)).astype("uint8")
        self.inputs = {
            'Input': input,
            'WeightList': flat_w,
            'PreState': [('init_h', init_h), ('init_c', init_c)],
            'SequenceLength': self.sequence_length
        }
        if self.sequence_length is None:
            self.inputs = {
                'Input': input,
                'WeightList': flat_w,
                'PreState': [('init_h', init_h), ('init_c', init_c)],
            }
        self.attrs = {
            'dropout_prob': self.dropout,
            'is_bidirec': self.is_bidirec,
            'input_size': input_size,
            'hidden_size': hidden_size,
            'num_layers': self.num_layers,
            'mode': self.mode,
            'is_test': self.is_test
        }
        self.outputs = {
            'Out': output,
            "State": [('last_hidden', last_hidden), ('last_cell', last_cell)],
            'Reserve': np.ndarray((400)).astype("uint8"),
            'DropoutState': state_out
        }
    def test_output(self):
        self.check_output(no_check_set=['Reserve', 'DropoutState'])
    def set_attrs(self):
        pass
    def test_grad(self):
        if not self.is_test:
            var_name_list = self.get_weight_names()
            grad_check_list = ['Input', 'init_h', 'init_c']
            grad_check_list.extend(var_name_list)
            self.check_grad(set(grad_check_list),
                            ['Out', 'last_hidden', 'last_cell'])
 class TestRNNOp1(TestRNNOp):
    def set_attrs(self):
        self.sequence_length = None
 class TestRNNOp2(TestRNNOp):
    def set_attrs(self):
        self.sequence_length = None
        self.is_bidirec = True
 class TestRNNOp3(TestRNNOp):
    def set_attrs(self):
        self.is_test = True
        self.sequence_length = None
 class TestRNNOp4(TestRNNOp):
    def set_attrs(self):
        self.is_test = True
        self.sequence_length = None
        self.is_bidirec = True
 class TestRNNOp5(TestRNNOp):
    def set_attrs(self):
        self.num_layers = 2
 class TestRNNOp6(TestRNNOp):
    def set_attrs(self):
        self.num_layers = 2
        self.is_bidirec = True
 class TestRNNOp7(TestRNNOp):
    def set_attrs(self):
        self.num_layers = 2
        self.is_bidirec = True
        self.is_test = True
 class TestRNNOp8(TestRNNOp):
    def set_attrs(self):
        self.num_layers = 2
        self.is_bidirec = True
        self.sequence_length = None
 class TestRNNOp9(TestRNNOp):
    def set_attrs(self):
        self.num_layers = 3
 if __name__ == '__main__':
    unittest.main()
--- a/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
+++ b/python/paddle/fluid/transpiler/memory_optimization_transpiler.py
@@ -21,7 +21,7 @@ def memory_optimize(input_program,
                    level=0,
                    skip_grads=True):
    """
 	:api_attr: Static Graph
    This API is deprecated since 1.6. Please do not use it. The better
    memory optimization strategies are enabled by default.
@@ -43,7 +43,7 @@ def memory_optimize(input_program,
 def release_memory(input_program, skip_opt_set=None):
    """
 	:api_attr: Static Graph
    This API is deprecated since 1.6. Please do not use it. The better
    memory optimization strategies are enabled by default.