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# 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 unittest
import numpy as np
import math
from op_test import OpTest
from test_lstm_op import identity, sigmoid, tanh, relu
class TestGRUOp(OpTest):
lod = [[0, 2, 6, 9]]
batch_size = lod[0][-1]
frame_size = 5
activate = {
'identity': identity,
'sigmoid': sigmoid,
'tanh': tanh,
'relu': relu
}
@staticmethod
def seq_to_batch(lod, is_reverse):
idx_in_seq_list = []
seq_starts = lod[0]
seq_lens = []
for i in range(len(seq_starts) - 1):
seq_lens.append(seq_starts[i + 1] - seq_starts[i])
sorted_seqs = sorted(
range(len(seq_lens)), lambda x, y: seq_lens[y] - seq_lens[x])
num_batch = seq_lens[sorted_seqs[0]]
for batch_idx in range(num_batch):
idx_in_seq = []
for i in range(len(seq_lens)):
if seq_lens[sorted_seqs[i]] <= batch_idx:
break
idx = (seq_starts[sorted_seqs[i] + 1] - 1 - batch_idx
) if is_reverse else (
seq_starts[sorted_seqs[i]] + batch_idx)
idx_in_seq.append(idx)
idx_in_seq_list.append(idx_in_seq)
return idx_in_seq_list, sorted_seqs
def gru_step(self, x, h_p, w, b):
batch_size = x.shape[0]
frame_size = w.shape[0]
g = x + np.tile(b, (batch_size, 1))
w_u_r = w.flatten()[:frame_size * frame_size * 2].reshape(
(frame_size, frame_size * 2))
u_r = self.activate[self.attrs['gate_activation']](np.dot(
h_p, w_u_r) + g[:, :frame_size * 2])
u = u_r[:, :frame_size]
r = u_r[:, frame_size:frame_size * 2]
r_h_p = r * h_p
w_c = w.flatten()[frame_size * frame_size * 2:].reshape(
(frame_size, frame_size))
c = self.activate[self.attrs['activation']](np.dot(r_h_p, w_c) +
g[:, frame_size * 2:])
g = np.hstack((u_r, c))
h = u * c + (1 - u) * h_p
return g, r_h_p, h
def gru(self):
input, lod = self.inputs['Input']
w = self.inputs['Weight']
b = self.inputs['Bias'] if self.inputs.has_key('Bias') else np.zeros(
(1, self.frame_size * 3))
batch_gate = self.outputs['BatchGate']
batch_reset_hidden_prev = self.outputs['BatchResetHiddenPrev']
batch_hidden = self.outputs['BatchHidden']
hidden = self.outputs['Hidden']
idx_in_seq_list = self.idx_in_seq_list
h_p = self.inputs['H0'][self.sorted_seqs] if self.inputs.has_key(
'H0') else np.zeros((len(idx_in_seq_list[0]), self.frame_size))
num_batch = len(idx_in_seq_list)
end_idx = 0
for batch_idx in range(num_batch):
x = input[idx_in_seq_list[batch_idx]]
g, r_h_p, h = self.gru_step(x, h_p, w, b)
if batch_idx < (num_batch - 1):
h_p = h[:len(idx_in_seq_list[batch_idx + 1])]
start_idx = end_idx
end_idx = start_idx + len(idx_in_seq_list[batch_idx])
batch_gate[start_idx:end_idx] = g
batch_reset_hidden_prev[start_idx:end_idx] = r_h_p
batch_hidden[start_idx:end_idx] = h
hidden[idx_in_seq_list[batch_idx]] = h
return batch_gate, batch_reset_hidden_prev, hidden
def set_data(self):
lod = self.lod
self.idx_in_seq_list, self.sorted_seqs = self.seq_to_batch(
lod, self.is_reverse)
batch_size = self.batch_size
frame_size = self.frame_size
input = np.random.rand(batch_size, frame_size * 3).astype('float64')
h0 = np.random.rand(len(self.idx_in_seq_list[0]),
frame_size).astype('float64')
weight = np.random.rand(frame_size, frame_size * 3).astype('float64')
bias = np.random.rand(1, frame_size * 3).astype('float64')
self.inputs = {
'Input': (input, lod),
'H0': h0,
'Weight': weight,
'Bias': bias
}
self.outputs = {
'BatchGate': np.zeros(
(batch_size, frame_size * 3), dtype='float64'),
'BatchResetHiddenPrev': np.zeros(
(batch_size, frame_size), dtype='float64'),
'BatchHidden': np.zeros(
(batch_size, frame_size), dtype='float64'),
'Hidden': np.zeros(
(batch_size, frame_size), dtype='float64')
}
def set_confs(self):
self.is_reverse = False
self.attrs = {
'activation': 'tanh',
'gate_activation': 'sigmoid',
'is_reverse': self.is_reverse
}
def setUp(self):
self.op_type = "gru"
self.set_confs()
self.set_data()
self.gru()
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['Input', 'H0', 'Weight', 'Bias'], ['Hidden'])
class TestGRUOpNoInitial(TestGRUOp):
def set_data(self):
super(TestGRUOpNoInitial, self).set_data()
self.inputs.pop('H0')
def test_check_grad(self):
self.check_grad(['Input', 'Weight', 'Bias'], ['Hidden'])
class TestGRUOpReverse(TestGRUOp):
def set_confs(self):
self.is_reverse = True
self.attrs = {
'activation': 'tanh',
'gate_activation': 'sigmoid',
'is_reverse': self.is_reverse
}
if __name__ == "__main__":
unittest.main()
