test_basic_gru_api.py

# 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 unittest
import numpy
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from paddle.fluid.contrib.layers import basic_gru
from paddle.fluid.executor import Executor
from paddle.fluid import framework

import numpy as np

np.set_seed(123)

SIGMOID_THRESHOLD_MIN = -40.0
SIGMOID_THRESHOLD_MAX = 13.0
EXP_MAX_INPUT = 40.0


def sigmoid(x):
    y = np.copy(x)
    y[x < SIGMOID_THRESHOLD_MIN] = SIGMOID_THRESHOLD_MIN
    y[x > SIGMOID_THRESHOLD_MAX] = SIGMOID_THRESHOLD_MAX
    return 1. / (1. + np.exp(-y))


def tanh(x):
    y = -2. * x
    y[y > EXP_MAX_INPUT] = EXP_MAX_INPUT
    return (2. / (1. + np.exp(y))) - 1.


def gru_np(input,
           init_h,
           hidden_size,
           gate_weight,
           gate_bias,
           candidate_weight,
           candidate_bias,
           num_layers=1,
           batch_first=False,
           is_bidirect=False,
           sequence_length=None):

    def step(step_in, pre_hidden, gate_w, gate_b, candidate_w, candidate_b):
        concat_1 = np.concatenate([step_in, pre_hidden], 1)

        gate_input = np.matmul(concat_1, gate_w)
        gate_input += gate_b
        gate_input = sigmoid(gate_input)
        r, u = np.split(gate_input, indices_or_sections=2, axis=1)

        r_hidden = r * pre_hidden

        candidate = np.matmul(np.concatenate([step_in, r_hidden], 1),
                              candidate_w)

        candidate += candidate_b
        c = tanh(candidate)

        new_hidden = u * pre_hidden + (1 - u) * c

        return new_hidden

    if batch_first:
        input = np.tranpose(input, [1, 0, 2])

    batch_size = input.shape[1]
    mask = None
    if sequence_length is not None:
        max_seq_len = input.shape[0]

        mask = np.zeros([batch_size, max_seq_len])

        for i, len in enumerate(sequence_length):
            mask[i, :len] = 1.0

        mask = np.transpose(mask, [1, 0])

    direc_num = 1
    if is_bidirect:
        direc_num = 2
    if init_h:
        init_h = np.reshape(init_h,
                            shape=[num_layers, direc_num, -1, hidden_size])
    else:
        init_h = np.zeros([num_layers, direc_num, batch_size, hidden_size])

    def get_single_direction_output(rnn_input, mask=None, direc_index=0):
        seq_len = rnn_input.shape[0]

        output = []
        # init pre hidden
        pre_hidden_array = []
        for i in range(num_layers):
            pre_hidden_array.append(init_h[i, direc_index])

        for i in range(seq_len):
            step_input = rnn_input[i]

            if mask is not None:
                step_mask = mask[i]
                step_mask = np.reshape(step_mask, [-1, 1])

            for i in range(num_layers):
                new_hidden = step(
                    step_input, pre_hidden_array[i],
                    gate_weight[direc_index * num_layers + i],
                    gate_bias[direc_index * num_layers + i],
                    candidate_weight[direc_index * num_layers + i],
                    candidate_bias[direc_index * num_layers + i])

                if mask is not None:
                    new_hidden = new_hidden * step_mask + (
                        1 - step_mask) * pre_hidden_array[i]

                pre_hidden_array[i] = new_hidden

                step_input = new_hidden
            output.append(step_input)
        rnn_out = np.concatenate(output, 0)
        rnn_out = np.reshape(rnn_out, [seq_len, -1, hidden_size])

        last_hidden_out = np.concatenate(pre_hidden_array, 0)
        last_hidden_out = np.reshape(last_hidden_out,
                                     [num_layers, -1, hidden_size])

        return rnn_out, last_hidden_out

    fw_rnn_out, fw_last_hidden = get_single_direction_output(input,
                                                             mask,
                                                             direc_index=0)

    if is_bidirect:
        bw_input = input[::-1]
        bw_mask = None
        if mask is not None:
            bw_mask = mask[::-1]

        bw_rnn_out, bw_last_hidden = get_single_direction_output(bw_input,
                                                                 bw_mask,
                                                                 direc_index=1)

        bw_rnn_out = bw_rnn_out[::-1]

        rnn_out = np.concatenate([fw_rnn_out, bw_rnn_out], 2)
        last_hidden = np.concatenate([fw_last_hidden, bw_last_hidden], 1)
        last_hidden = np.reshape(last_hidden,
                                 [num_layers * direc_num, -1, hidden_size])

        if batch_first:
            rnn_out = np.transpose(rnn_out, [1, 0, 2])

        return rnn_out, last_hidden
    else:
        rnn_out = fw_rnn_out
        last_hidden = fw_last_hidden

        if batch_first:
            rnn_out = np.transpose(rnn_out, [1, 0, 2])

        return rnn_out, last_hidden


class TestBasicGRUApi(unittest.TestCase):

    def setUp(self):
        self.hidden_size = 10
        self.batch_size = 5
        self.seq_len = 6
        self.num_layers = 2
        self.is_bidirect = True
        self.batch_first = False

    def test_run(self):
        x = layers.data(name='x',
                        shape=[-1, self.batch_size, self.hidden_size],
                        dtype='float32')
        sequence_length = layers.data(name="sequence_length",
                                      shape=[-1],
                                      dtype='float32')

        rnn_out, last_hidden = basic_gru( x, None, self.hidden_size, num_layers=self.num_layers, \
                batch_first = self.batch_first, bidirectional=self.is_bidirect, sequence_length=sequence_length )

        last_hidden.persisbale = True
        rnn_out.persisbale = True

        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        exe = Executor(place)
        exe.run(framework.default_startup_program())

        param_list = fluid.default_main_program().block(0).all_parameters()

        # process weight and bias
        gate_weight = []
        gate_bias = []
        candidate_weight = []
        candidate_bias = []

        for i in range(self.num_layers):
            gate_w_name = "basic_gru_layers_" + str(i) + "/BasicGRUUnit_0.w_0"
            gate_b_name = "basic_gru_layers_" + str(i) + "/BasicGRUUnit_0.b_0"
            candidate_w_name = "basic_gru_layers_" + str(
                i) + "/BasicGRUUnit_0.w_1"
            candidate_b_name = "basic_gru_layers_" + str(
                i) + "/BasicGRUUnit_0.b_1"

            gate_w = np.array(
                fluid.global_scope().find_var(gate_w_name).get_tensor())
            gate_w = np.random.uniform(-0.1, 0.1,
                                       size=gate_w.shape).astype('float32')
            fluid.global_scope().find_var(gate_w_name).get_tensor().set(
                gate_w, place)

            gate_b = np.array(
                fluid.global_scope().find_var(gate_b_name).get_tensor())
            gate_b = np.random.uniform(-0.1, 0.1,
                                       size=gate_b.shape).astype('float32')
            fluid.global_scope().find_var(gate_b_name).get_tensor().set(
                gate_b, place)

            candidate_w = np.array(
                fluid.global_scope().find_var(candidate_w_name).get_tensor())
            candidate_w = np.random.uniform(
                -0.1, 0.1, size=candidate_w.shape).astype('float32')
            fluid.global_scope().find_var(candidate_w_name).get_tensor().set(
                candidate_w, place)

            candidate_b = np.array(
                fluid.global_scope().find_var(candidate_b_name).get_tensor())
            candidate_b = np.random.uniform(
                -0.1, 0.1, size=candidate_b.shape).astype('float32')
            fluid.global_scope().find_var(candidate_b_name).get_tensor().set(
                candidate_b, place)

            gate_weight.append(gate_w)
            gate_bias.append(gate_b)
            candidate_weight.append(candidate_w)
            candidate_bias.append(candidate_b)

        if self.is_bidirect:
            for i in range(self.num_layers):
                gate_w_name = "basic_gru_reverse_layers_" + str(
                    i) + "/BasicGRUUnit_0.w_0"
                gate_b_name = "basic_gru_reverse_layers_" + str(
                    i) + "/BasicGRUUnit_0.b_0"
                candidate_w_name = "basic_gru_reverse_layers_" + str(
                    i) + "/BasicGRUUnit_0.w_1"
                candidate_b_name = "basic_gru_reverse_layers_" + str(
                    i) + "/BasicGRUUnit_0.b_1"

                gate_w = np.array(
                    fluid.global_scope().find_var(gate_w_name).get_tensor())
                gate_w = np.random.uniform(-0.1, 0.1,
                                           size=gate_w.shape).astype('float32')
                fluid.global_scope().find_var(gate_w_name).get_tensor().set(
                    gate_w, place)

                gate_b = np.array(
                    fluid.global_scope().find_var(gate_b_name).get_tensor())
                gate_b = np.random.uniform(-0.1, 0.1,
                                           size=gate_b.shape).astype('float32')
                fluid.global_scope().find_var(gate_b_name).get_tensor().set(
                    gate_b, place)

                candidate_w = np.array(fluid.global_scope().find_var(
                    candidate_w_name).get_tensor())
                candidate_w = np.random.uniform(
                    -0.1, 0.1, size=candidate_w.shape).astype('float32')
                fluid.global_scope().find_var(
                    candidate_w_name).get_tensor().set(candidate_w, place)

                candidate_b = np.array(fluid.global_scope().find_var(
                    candidate_b_name).get_tensor())
                candidate_b = np.random.uniform(
                    -0.1, 0.1, size=candidate_b.shape).astype('float32')
                fluid.global_scope().find_var(
                    candidate_b_name).get_tensor().set(candidate_b, place)

                gate_weight.append(gate_w)
                gate_bias.append(gate_b)
                candidate_weight.append(candidate_w)
                candidate_bias.append(candidate_b)

        step_input_np = np.random.uniform(
            -0.1, 0.1,
            (self.seq_len, self.batch_size, self.hidden_size)).astype('float32')
        sequence_length_np = np.random.randint(
            self.seq_len // 2, self.seq_len,
            size=(self.batch_size)).astype('int64')

        out = exe.run(feed={
            'x': step_input_np,
            'sequence_length': sequence_length_np
        },
                      fetch_list=[rnn_out, last_hidden])

        api_rnn_out = out[0]
        api_last_hidden = out[1]

        np_out = gru_np(step_input_np,
                        None,
                        self.hidden_size,
                        gate_weight,
                        gate_bias,
                        candidate_weight,
                        candidate_bias,
                        num_layers=self.num_layers,
                        batch_first=self.batch_first,
                        is_bidirect=self.is_bidirect,
                        sequence_length=sequence_length_np)

        self.assertTrue(np.allclose(api_rnn_out, np_out[0], rtol=1e-4, atol=0))

        self.assertTrue(
            np.allclose(api_last_hidden, np_out[1], rtol=1e-4, atol=0))


if __name__ == '__main__':
    unittest.main()