# 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 contextlib import unittest import numpy as np import paddle.fluid as fluid from paddle.fluid import core from paddle.fluid.imperative.nn import FC from test_imperative_base import new_program_scope class MyLayer(fluid.imperative.Layer): def __init__(self, name_scope): super(MyLayer, self).__init__(name_scope) def forward(self, inputs): x = fluid.layers.relu(inputs) self._x_for_debug = x x = fluid.layers.elementwise_mul(x, x) x = fluid.layers.reduce_sum(x) return [x] class MyPyLayer(fluid.imperative.PyLayer): def __init__(self): super(MyPyLayer, self).__init__() @staticmethod def forward(inputs): return np.tanh(inputs[0]) @staticmethod def backward(inputs): inp, out, dout = inputs return np.array(dout) * (1 - np.square(np.array(out))) class MLP(fluid.imperative.Layer): def __init__(self, name_scope): super(MLP, self).__init__(name_scope) self._fc1 = FC(self.full_name(), 3, fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.1))) self._fc2 = FC(self.full_name(), 4, fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.1))) def forward(self, inputs): x = self._fc1(inputs) x = self._fc2(x) x = fluid.layers.reduce_sum(x) return x class SimpleRNNCell(fluid.imperative.Layer): def __init__(self, name_scope, step_input_size, hidden_size, output_size, param_attr): super(SimpleRNNCell, self).__init__(name_scope) self.step_input_size = step_input_size self.hidden_size = hidden_size self.output_size = output_size self._dype = core.VarDesc.VarType.FP32 from paddle.fluid.layer_helper import LayerHelper self._helper = LayerHelper( 'SimpleRNNCell', act="tanh", param_attr=param_attr) def _build_once(self, inputs, pre_hidden): i2h_param_shape = [self.step_input_size, self.hidden_size] h2h_param_shape = [self.hidden_size, self.hidden_size] h2o_param_shape = [self.output_size, self.hidden_size] self._i2h_w = self._helper.create_parameter( attr=self._helper.param_attr, shape=i2h_param_shape, dtype=self._dtype, is_bias=False) self._h2h_w = self._helper.create_parameter( attr=self._helper.param_attr, shape=h2h_param_shape, dtype=self._dtype, is_bias=False) self._h2o_w = self._helper.create_parameter( attr=self._helper.param_attr, shape=h2o_param_shape, dtype=self._dtype, is_bias=False) def forward(self, input, pre_hidden): tmp_i2h = self._helper.create_variable_for_type_inference(self._dtype) tmp_h2h = self._helper.create_variable_for_type_inference(self._dtype) hidden = self._helper.create_variable_for_type_inference(self._dype) out = self._helper.create_variable_for_type_inference(self._dype) softmax_out = self._helper.create_variable_for_type_inference( self._dtype) reduce_out = self._helper.create_variable_for_type_inference( self._dtype) self._helper.append_op( type="mul", inputs={"X": input, "Y": self._i2h_w}, outputs={"Out": tmp_i2h}, attrs={"x_num_col_dims": 1, "y_num_col_dims": 1}) self._helper.append_op( type="mul", inputs={"X": pre_hidden, "Y": self._h2h_w}, outputs={"Out": tmp_h2h}, attrs={"x_num_col_dims": 1, "y_num_col_dims": 1}) self._helper.append_op( type="elementwise_add", inputs={'X': tmp_h2h, 'Y': tmp_i2h}, outputs={'Out': hidden}, attrs={'axis': -1, 'use_mkldnn': False}) hidden = self._helper.append_activation(hidden) self._helper.append_op( type="mul", inputs={"X": hidden, "Y": self._h2o_w}, outputs={"Out": out}, attrs={"x_num_col_dims": 1, "y_num_col_dims": 1}) self._helper.append_op( type="softmax", inputs={"X": out}, outputs={"Out": softmax_out}, attrs={"use_cudnn": False}) self._helper.append_op( type='reduce_sum', inputs={'X': softmax_out}, outputs={'Out': reduce_out}, attrs={'dim': None, 'keep_dim': False, 'reduce_all': True}) return reduce_out, hidden class SimpleRNN(fluid.imperative.Layer): def __init__(self, name_scope): super(SimpleRNN, self).__init__(name_scope) self.seq_len = 4 self._cell = SimpleRNNCell( self.full_name(), 3, 3, 3, fluid.ParamAttr(initializer=fluid.initializer.Constant(value=0.1))) def forward(self, inputs): outs = list() pre_hiddens = list() init_hidden = fluid.layers.tensor.create_parameter( attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.1)), shape=[1, 3], dtype='float32', is_bias=False) pre_hidden = init_hidden for i in range(self.seq_len): input = fluid.layers.slice( inputs, axes=[1], starts=[i], ends=[i + 1]) input = fluid.layers.reshape(input, shape=[1, 3]) out_softmax, pre_hidden = self._cell(input, pre_hidden) outs.append(out_softmax) return outs, pre_hiddens # class TestImperative(unittest.TestCase): # def test_sum_op(self): # x = np.ones([2, 2], np.float32) # with fluid.imperative.guard(): # inputs = [] # for _ in range(10): # inputs.append(fluid.imperative.base.to_variable(x)) # ret = fluid.layers.sums(inputs) # loss = fluid.layers.reduce_sum(ret) # loss._backward() # self.assertTrue(np.allclose(ret._numpy(), x * 10)) # self.assertTrue(np.allclose(inputs[0]._gradient(), x)) # def test_layer(self): # with fluid.imperative.guard(): # cl = core.Layer() # cl.forward([]) # l = fluid.imperative.Layer("l") # self.assertRaises(NotImplementedError, l.forward, []) # def test_layer_in_out(self): # np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32) # with fluid.imperative.guard(): # var_inp = fluid.imperative.base.to_variable(np_inp) # l = MyLayer("my_layer") # x = l(var_inp)[0] # self.assertIsNotNone(x) # dy_out = x._numpy() # x._backward() # dy_grad = l._x_for_debug._gradient() # with new_program_scope(): # inp = fluid.layers.data(name="inp", shape=[3], append_batch_size=False) # l = MyLayer("my_layer") # x = l(inp)[0] # param_grads = fluid.backward.append_backward(x, parameter_list=[l._x_for_debug.name])[0] # exe = fluid.Executor(fluid.CPUPlace( # ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) # static_out, static_grad = exe.run(feed={inp.name: np_inp}, # fetch_list=[x.name, param_grads[1].name]) # self.assertTrue(np.allclose(dy_out, static_out)) # self.assertTrue(np.allclose(dy_grad, static_grad)) # with fluid.imperative.guard(): # var_inp = fluid.imperative.base.to_variable(np_inp) # mlp = MLP("mlp") # out = mlp(var_inp) # dy_out = out._numpy() # out._backward() # dy_grad = mlp._fc1._w._gradient() # with new_program_scope(): # inp = fluid.layers.data( # name="inp", shape=[2, 2], append_batch_size=False) # mlp = MLP("mlp") # out = mlp(inp) # param_grads = fluid.backward.append_backward(out, parameter_list=[mlp._fc1._w.name])[0] # exe = fluid.Executor(fluid.CPUPlace( # ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) # exe.run(fluid.default_startup_program()) # static_out, static_grad = exe.run( # feed={inp.name: np_inp}, # fetch_list=[out.name, param_grads[1].name]) # self.assertTrue(np.allclose(dy_out, static_out)) # self.assertTrue(np.allclose(dy_grad, static_grad)) # params = mlp.parameters(True) # self.assertEqual("mlp/MLP_0/FC_0_0.w_0", params[0].name) # self.assertEqual("mlp/MLP_0/FC_0_0.b_0", params[1].name) # self.assertEqual("mlp/MLP_0/FC_1_0.w_0", params[2].name) # self.assertEqual("mlp/MLP_0/FC_1_0.b_0", params[3].name) # self.assertEqual(len(params), 4) # sublayers = mlp.sublayers(True) # self.assertEqual(mlp._fc1, sublayers[0]) # self.assertEqual(mlp._fc2, sublayers[1]) # self.assertEqual(len(sublayers), 2) # def test_rnn(self): # np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], # [10.0, 11.0, 12.0]]) # np_inp = np_inp.reshape((1, 4, 3)) # np_inp = np_inp.astype(np.float32) # with fluid.imperative.guard(): # var_inp = fluid.imperative.base.to_variable(np_inp) # var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3]) # simple_rnn = SimpleRNN("simple_rnn") # outs, pre_hiddens = simple_rnn.forward(var_inp) # dy_out = outs[3]._numpy() # outs[3]._backward() # dy_grad_h2o = simple_rnn._cell._h2o_w._gradient() # dy_grad_h2h = simple_rnn._cell._h2h_w._gradient() # dy_grad_i2h = simple_rnn._cell._i2h_w._gradient() # with new_program_scope(): # inp = fluid.layers.data( # name="inp", shape=[1, 4, 3], append_batch_size=False) # simple_rnn = SimpleRNN("simple_rnn") # outs, pre_hiddens = simple_rnn(inp) # param_grads = fluid.backward.append_backward(outs[3]) # exe = fluid.Executor(fluid.CPUPlace()) # exe.run(fluid.default_startup_program()) # static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run( # feed={inp.name: np_inp}, # fetch_list=[ # outs[3].name, param_grads[0][1].name, # param_grads[1][1].name, param_grads[2][1].name # ]) # self.assertTrue(np.allclose(dy_out, static_out)) # self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o)) # self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h)) # self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h)) class TestImperativePyLayer(unittest.TestCase): def test_pylayer_func_id(self): with fluid.imperative.guard(): class PyLayer1(fluid.imperative.PyLayer): def __init__(self): super(PyLayer1, self).__init__() @staticmethod def forward(input): return input @staticmethod def backward(input): return input class PyLayer2(fluid.imperative.PyLayer): def __init__(self): super(PyLayer2, self).__init__() @staticmethod def forward(input): return input @staticmethod def backward(input): return input py_layer_1 = PyLayer1() py_layer_2 = PyLayer2() py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2]))) py_layer_2(fluid.imperative.base.to_variable(np.ones([2, 2]))) id = py_layer_1.forward_id self.assertGreater(id, 0) self.assertEqual(py_layer_1.backward_id, id + 1) self.assertEqual(py_layer_2.forward_id, id + 2) self.assertEqual(py_layer_2.backward_id, id + 3) py_layer_1(fluid.imperative.base.to_variable(np.ones([2, 2]))) self.assertEqual(py_layer_1.forward_id, id) def test_pylayer(self): np_inp = np.ones([2, 2], np.float32) with fluid.imperative.guard(): my_py_layer = MyPyLayer() var_inp = fluid.imperative.base.to_variable(np_inp) outs = my_py_layer(var_inp) dy_out = np.sum(outs[0]._numpy()) outs[0]._backward() dy_grad = var_inp._gradient() with new_program_scope(): inp = fluid.layers.data( name="inp", shape=[2, 2], append_batch_size=False) # TODO(panyx0718): Paddle doesn't diff against data `inp`. x1 = inp * 1 # TODO(panyx0718): If reduce_sum is skipped, the result is wrong. x = fluid.layers.reduce_sum(fluid.layers.tanh(x1)) param_grads = fluid.backward.append_backward( x, parameter_list=[x1.name])[0] exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) static_out, static_grad = exe.run( feed={inp.name: np_inp}, fetch_list=[x.name, param_grads[1].name]) self.assertTrue(np.allclose(dy_out, static_out)) self.assertTrue(np.allclose(dy_grad, static_grad)) if __name__ == '__main__': unittest.main()