import unittest import paddle.v2.fluid.layers as layers from paddle.v2.fluid.framework import Program, grad_var_name from paddle.v2.fluid.executor import Executor from paddle.v2.fluid.backward import append_backward import numpy as np import paddle.v2.fluid.core as core class PyRNNBase(object): def __init__(self, input_shape, output_shape): self.x = np.ones(shape=input_shape).astype("float32") self.y = np.zeros(shape=output_shape).astype("float32") def step(self, step_id, x): raise NotImplementedError def forward(self): for step_id in range(self.x.shape[0]): self.step(step_id, self.x[step_id]) return np.array([np.mean(self.y)]) def segment_inputs(self): return [self.x[i] for i in range(self.x.shape[0])] class PySimpleRNN1(PyRNNBase): def __init__(self, input_shape, output_shape): super(PySimpleRNN1, self).__init__(input_shape, output_shape) seq_len, batch_size, input_dim = input_shape self.h_boot = np.random.normal(size=(batch_size, input_dim)).astype("float32") self.scale = 1.0 / 2.0 men_dim = (seq_len, batch_size, input_dim) self.mems = np.zeros(shape=men_dim).astype("float32") def step(self, step_id, x): if step_id == 0: pre_mem = self.h_boot else: pre_mem = self.mems[step_id - 1] self.mems[step_id] = (pre_mem + x) * self.scale self.y[step_id] = self.mems[step_id] class PySimpleRNN2(PyRNNBase): def __init__(self, input_shape, output_shape): super(PySimpleRNN2, self).__init__(input_shape, output_shape) seq_len, batch_size, input_dim = input_shape self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32") self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32") self.h_boot = np.ones(shape=(batch_size, input_dim)).astype("float32") men_dim = (seq_len, batch_size, input_dim) self.mems = np.zeros(shape=men_dim).astype("float32") def step(self, step_id, x): if step_id > 0: pre_mem = self.mems[step_id - 1] else: pre_mem = self.h_boot xW = np.matmul(x, self.W).astype("float32") hU = np.matmul(pre_mem, self.U).astype("float32") def py_sigmoid(x): return 1. / (1. + np.exp(-x)) self.mems[step_id] = py_sigmoid(xW + hU) self.y[step_id] = self.mems[step_id] def create_tensor(np_data, place): tensor = core.LoDTensor() tensor.set(np_data, place) return tensor class RecurrentOpTest1(unittest.TestCase): ''' Test RNNOp equation: h_t = ( x_t + h_{t-1} ) / scale vars: - x memories: - h outputs: - h ''' input_dim = 2 batch_size = 1 sent_len = 1 def setup_program(self): self.main_program = Program() self.startup_program = Program() self.p_info = { "main_program": self.main_program, "startup_program": self.startup_program } self.place = core.CPUPlace() def setUp(self): self.setup_program() self.data_field = {"x", "h_boot"} self.input_shape = (self.sent_len, self.batch_size, self.input_dim) self.output_shape = (self.sent_len, self.batch_size, self.input_dim) self.py_rnn = PySimpleRNN1(self.input_shape, self.output_shape) self.output = layers.mean(x=self.create_rnn_op(), **self.p_info) def create_rnn_op(self): x = layers.data( shape=[self.sent_len, self.batch_size, self.input_dim], dtype='float32', name='x', append_batch_size=False, **self.p_info) x.stop_gradient = False h_boot = layers.data( shape=[self.input_dim], dtype='float32', name='h_boot', **self.p_info) h_boot.stop_gradient = False rnn = layers.StaticRNN(main_program=self.main_program) with rnn.step(): h_pre = rnn.memory(init=h_boot) x_t = rnn.step_input(x) h = layers.scale( x=layers.elementwise_add( x=h_pre, y=x_t, **self.p_info), scale=self.py_rnn.scale, **self.p_info) rnn.update_memory(h_pre, h) rnn.output(h) return rnn() def forward(self): self.feed_map = { x: create_tensor(getattr(self.py_rnn, x), self.place) for x in self.data_field } exe = Executor(self.place) out = exe.run(self.main_program, feed=self.feed_map, fetch_list=[self.output]) return out[0] def backward(self): self.feed_map = { x: create_tensor(getattr(self.py_rnn, x), self.place) for x in self.data_field } fetch_list = [ self.main_program.global_block().var(grad_var_name(x)) for x in self.data_field ] exe = Executor(self.place) return exe.run(self.main_program, feed=self.feed_map, fetch_list=fetch_list, return_numpy=False) def test_backward(self): self.check_forward() append_backward(self.output) ana_grad = [np.array(x) for x in self.backward()] num_grad = self.get_numerical_gradient() for idx, name in enumerate(self.data_field): self.assertEqual(num_grad[idx].shape, ana_grad[idx].shape) self.assertTrue( np.isclose( num_grad[idx], ana_grad[idx], rtol=0.1).all()) def check_forward(self): print 'test recurrent op forward' pd_output = self.forward() py_output = self.py_rnn.forward() print 'pd_output', pd_output print print 'py_output', py_output self.assertEqual(pd_output.shape, py_output.shape) self.assertTrue(np.isclose(pd_output, py_output, rtol=0.1).all()) def get_numerical_gradient(self, delta=0.005): dloss_dout = 1.0 feed_list = [getattr(self.py_rnn, x) for x in self.data_field] grad_list = [np.zeros_like(x) for x in feed_list] for feed, grad in zip(feed_list, grad_list): for f, g in np.nditer([feed, grad], op_flags=['readwrite']): o = float(f) f[...] = o + delta y_pos = self.forward() f[...] = o - delta y_neg = self.forward() f[...] = o dout_dfeed = (y_pos - y_neg) / (delta * 2) g[...] = dout_dfeed[0] return grad_list class RecurrentOpTest2(RecurrentOpTest1): ''' Test RNNOp equation: h_t = \sigma (W x_t + U h_{t-1}) weights: - W - U vars: - x memories: - h outputs: - h ''' input_dim = 2 batch_size = 10 sent_len = 2 def setUp(self): self.setup_program() self.data_field = {"x", "h_boot", "W", "U"} self.input_shape = (self.sent_len, self.batch_size, self.input_dim) self.output_shape = (self.sent_len, self.batch_size, self.input_dim) self.py_rnn = PySimpleRNN2(self.input_shape, self.output_shape) self.output = layers.mean(x=self.create_rnn_op(), **self.p_info) def create_rnn_op(self): x = layers.data( shape=[self.sent_len, self.batch_size, self.input_dim], dtype='float32', name='x', append_batch_size=False, **self.p_info) x.stop_gradient = False h_boot = layers.data( shape=[self.input_dim], dtype='float32', name='h_boot', **self.p_info) h_boot.stop_gradient = False rnn = layers.StaticRNN(main_program=self.main_program) with rnn.step(): h_pre = rnn.memory(init=h_boot) x_t = rnn.step_input(x) temp_l = layers.fc(input=x_t, size=self.input_dim, param_attr='W', bias_attr=False, **self.p_info) temp_r = layers.fc(input=h_pre, size=self.input_dim, param_attr='U', bias_attr=False, **self.p_info) h = layers.sigmoid( x=layers.elementwise_add( x=temp_l, y=temp_r, **self.p_info), **self.p_info) rnn.update_memory(h_pre, h) rnn.output(h) return rnn() class RecurrentOpMultipleMemoryTest(RecurrentOpTest1): ''' Test RNNOp with two memories equation: h_1 = h_pre_1 h_2 = h_pre_2 y = h_1 + h_2 vars: - x memories: - h_1, h_2 outputs: - y ''' class PySimpleRNN3(PyRNNBase): def __init__(self, input_shape, output_shape): super(RecurrentOpMultipleMemoryTest.PySimpleRNN3, self).__init__( input_shape, output_shape) seq_len, batch_size, input_dim = input_shape self.h_boot1 = np.random.normal(size=(batch_size, input_dim)).astype("float32") self.h_boot2 = np.random.normal(size=(batch_size, input_dim)).astype("float32") men_dim = (seq_len, batch_size, input_dim) self.mems1 = np.zeros(shape=men_dim).astype("float32") self.mems2 = np.zeros(shape=men_dim).astype("float32") def step(self, step_id, x): if step_id == 0: pre_mem1 = self.h_boot1 pre_mem2 = self.h_boot2 else: pre_mem1 = self.mems1[step_id - 1] pre_mem2 = self.mems2[step_id - 1] self.mems1[step_id] = pre_mem1 self.mems2[step_id] = pre_mem2 self.y[step_id] = self.mems1[step_id] + self.mems2[step_id] + x input_dim = 1 batch_size = 1 sent_len = 2 def setUp(self): self.setup_program() self.data_field = {"x", "h_boot1", "h_boot2"} self.input_shape = (self.sent_len, self.batch_size, self.input_dim) self.output_shape = (self.sent_len, self.batch_size, self.input_dim) self.py_rnn = RecurrentOpMultipleMemoryTest.PySimpleRNN3( self.input_shape, self.output_shape) self.output = layers.mean(x=self.create_rnn_op(), **self.p_info) def create_rnn_op(self): x = layers.data( shape=[self.sent_len, self.batch_size, self.input_dim], dtype='float32', name='x', append_batch_size=False, **self.p_info) x.stop_gradient = False h_boot1 = layers.data( shape=[self.batch_size, self.input_dim], dtype='float32', name='h_boot1', append_batch_size=False, **self.p_info) h_boot1.stop_gradient = False h_boot2 = layers.data( shape=[self.batch_size, self.input_dim], dtype='float32', name='h_boot2', append_batch_size=False, **self.p_info) h_boot2.stop_gradient = False rnn = layers.StaticRNN(main_program=self.main_program) with rnn.step(): h_pre1 = rnn.memory(init=h_boot1) h_pre2 = rnn.memory(init=h_boot2) x_t = rnn.step_input(x) mem1 = layers.scale(x=h_pre1, scale=1.0, **self.p_info) mem2 = layers.scale(x=h_pre2, scale=1.0, **self.p_info) out = layers.sums(input=[mem1, x_t, mem2], **self.p_info) rnn.update_memory(h_pre1, mem1) rnn.update_memory(h_pre2, mem2) rnn.output(out) return rnn() class RecurrentOpNoMemBootTest(RecurrentOpTest1): ''' Test RNNOp with two memories equation: mem = x + mem_pre y = mem vars: - x memories: - mem outputs: - y ''' class PySimpleRNN4(PyRNNBase): def __init__(self, input_shape, output_shape): super(RecurrentOpNoMemBootTest.PySimpleRNN4, self).__init__( input_shape, output_shape) men_dim = input_shape self.mems = np.zeros(shape=men_dim).astype("float32") def step(self, step_id, x): if step_id == 0: pre_mem = np.zeros_like(x) else: pre_mem = self.mems[step_id - 1] self.mems[step_id] = pre_mem + x self.y[step_id] = self.mems[step_id] input_dim = 1 batch_size = 1 sent_len = 2 def setUp(self): self.setup_program() self.data_field = {"x"} self.input_shape = (self.sent_len, self.batch_size, self.input_dim) self.output_shape = (self.sent_len, self.batch_size, self.input_dim) self.py_rnn = RecurrentOpNoMemBootTest.PySimpleRNN4(self.input_shape, self.output_shape) self.output = layers.mean(x=self.create_rnn_op(), **self.p_info) print self.main_program def create_rnn_op(self): x = layers.data( shape=[self.sent_len, self.batch_size, self.input_dim], dtype='float32', name='x', append_batch_size=False, **self.p_info) x.stop_gradient = False rnn = layers.StaticRNN(main_program=self.main_program) with rnn.step(): mem_pre = rnn.memory(shape=[-1, self.input_dim], batch_ref=x) x_t = rnn.step_input(x) mem = layers.elementwise_add(x=mem_pre, y=x_t, **self.p_info) rnn.update_memory(mem_pre, mem) rnn.output(mem) return rnn() if __name__ == '__main__': # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/6152 exit(0) unittest.main()