# Copyright (c) 2019 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 numpy as np import paddle import paddle.fluid as fluid from paddle.fluid.dygraph import Linear from paddle.fluid.dygraph.base import to_variable import unittest class Test_Detach(unittest.TestCase): def generate_Data(self): data = np.array( [[1, 8, 3, 9], [7, 20, 9, 6], [4, 6, 8, 10]]).astype('float32') return data def no_detach_multi(self): data = self.generate_Data() with fluid.dygraph.guard(): linear_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(5.0)) linear_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(6.0)) linear = Linear( 4, 10, param_attr=linear_w_param_attrs, bias_attr=linear_b_param_attrs) linear1_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(7.0)) linear1_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(8.0)) linear1 = Linear( 10, 1, param_attr=linear1_w_param_attrs, bias_attr=linear1_b_param_attrs) linear2_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(9.0)) linear2_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(10.0)) linear2 = Linear( 10, 1, param_attr=linear2_w_param_attrs, bias_attr=linear2_b_param_attrs) data = to_variable(data) x = linear(data) x1 = linear1(x) x2 = linear2(x) loss = x1 + x2 # print(loss, loss.shape) loss.backward() return x.gradient() def no_detach_single(self): data = self.generate_Data() with fluid.dygraph.guard(): linear_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(5.0)) linear_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(6.0)) linear = Linear( 4, 10, param_attr=linear_w_param_attrs, bias_attr=linear_b_param_attrs) linear1_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(7.0)) linear1_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(8.0)) linear1 = Linear( 10, 1, param_attr=linear1_w_param_attrs, bias_attr=linear1_b_param_attrs) data = to_variable(data) x = linear(data) x1 = linear1(x) loss = x1 # print(loss, loss.shape) loss.backward() return x.gradient() def detach_multi(self): data = self.generate_Data() with fluid.dygraph.guard(): linear_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(5.0)) linear_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(6.0)) linear = Linear( 4, 10, param_attr=linear_w_param_attrs, bias_attr=linear_b_param_attrs) linear1_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(7.0)) linear1_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(8.0)) linear1 = Linear( 10, 1, param_attr=linear1_w_param_attrs, bias_attr=linear1_b_param_attrs) linear2_w_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(9.0)) linear2_b_param_attrs = fluid.ParamAttr( initializer=fluid.initializer.Constant(10.0)) linear2 = Linear( 10, 1, param_attr=linear2_w_param_attrs, bias_attr=linear2_b_param_attrs) data = to_variable(data) x = linear(data) x_detach = x.detach() x1 = linear1(x) x2 = linear2(x_detach) loss = x1 + x2 # print(loss, loss.shape) loss.backward() return x.gradient() def test_NoDetachMulti_DetachMulti(self): array_no_detach_multi = self.no_detach_multi() array_detach_multi = self.detach_multi() assert not np.array_equal(array_no_detach_multi, array_detach_multi) def test_NoDetachSingle_DetachMulti(self): array_no_detach_single = self.no_detach_single() array_detach_multi = self.detach_multi() assert np.array_equal(array_no_detach_single, array_detach_multi) def test_detach_exception(self): x = fluid.layers.data(name="a", shape=[3, 4], dtype='float32') y = fluid.layers.fc(input=x, size=10, bias_attr=True) try: y_detach = y.detach() except Exception as e: # Here is to check assert type(e) == AssertionError assert str( e ) == "'detach' should be called by imperative Varible in imperative mode, please use fluid.dygraph.guard() as context to run it in imperative mode" class TestInplace(unittest.TestCase): def test_forward_version(self): with paddle.fluid.dygraph.guard(): var = paddle.to_tensor(np.ones((4, 2, 3)).astype(np.float32)) self.assertEqual(var.inplace_version, 0) detach_var_1 = var.detach() self.assertEqual(detach_var_1.inplace_version, 0) var[0] = 1.1 self.assertEqual(var.inplace_version, 1) detach_var_2 = var.detach() self.assertEqual(detach_var_2.inplace_version, 1) var[0] = 3 self.assertEqual(detach_var_1.inplace_version, 2) self.assertEqual(detach_var_2.inplace_version, 2) def test_backward_error(self): # It raises an error because the inplace operator will result # in incorrect gradient computation. with paddle.fluid.dygraph.guard(): var_a = paddle.ones(shape=[4, 2, 3], dtype="float32") var_a.stop_gradient = False var_b = var_a**2 # Here, the gradient computation will use the value of var_b var_c = var_b**2 detach_var_b = var_b.detach() detach_var_b[1:2] = 3.3 # var_b is modified inplace var_d = var_b**2 loss = paddle.nn.functional.relu(var_c + var_d) with self.assertRaisesRegexp( RuntimeError, "received tensor_version:{} != wrapper_version_snapshot:{}". format(1, 0)): loss.backward() if __name__ == '__main__': unittest.main()