Cao Ying
7 years ago
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/linear_chain_crf_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(
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linear_chain_crf,
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ops::LinearChainCRFOpKernel<paddle::platform::GPUPlace, float>,
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ops::LinearChainCRFOpKernel<paddle::platform::GPUPlace, double>);
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REGISTER_OP_GPU_KERNEL(
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linear_chain_crf_grad,
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ops::LinearChainCRFGradOpKernel<paddle::platform::GPUPlace, float>,
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ops::LinearChainCRFGradOpKernel<paddle::platform::GPUPlace, double>);
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import unittest
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import random
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import numpy as np
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from op_test import OpTest
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class LinearChainCrfForward(object):
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def __init__(self, seq_start_positions, emission_weights, emission_row_max,
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emission_exps, transition_weights, transition_exps, labels):
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self.tag_num = emission_weights.shape[1]
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self.seq_num = len(seq_start_positions) - 1
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self.seq_start_positions = seq_start_positions
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self.labels = labels
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self.x = emission_weights
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self.x_row_max = emission_row_max
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self.x_exps = emission_exps
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# unnormalized logits of the transition weights for the start mark.
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self.a = transition_weights[0, :]
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self.a_exps = transition_exps[0, :]
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# unnormalized logits of the transition weights for the end mark.
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self.b = transition_weights[1, :]
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self.b_exps = transition_exps[1, :]
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# unnormalized logits of the transition weights for all the other tags.
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self.w = transition_weights[2:, :]
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self.w_exps = transition_exps[2:, :]
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# The output of linear chain crf operator.
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# alpha is a memo table in dynamic programming to caculate
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# nomalization factor.
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self.alpha = np.zeros(
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(seq_start_positions[-1], self.tag_num), dtype="float64")
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self.log_likelihood = np.zeros((self.seq_num, 1))
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def _l1_norm(self, x):
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s = np.sum(x)
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x /= s
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return s
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def _forward_a_sequence(self, x, x_row_max, x_exps, label, alpha):
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seq_len = x_row_max.shape[0]
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log_likelihood = 0.
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for i in range(self.tag_num):
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alpha[0, i] = self.a_exps[i] * x_exps[0, i]
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log_likelihood = -x_row_max[0] - np.log(self._l1_norm(alpha[0, :]))
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# calculate the unnormalized logits of the normalization factor.
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for k in range(1, seq_len):
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for i in range(self.tag_num):
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s = 0.
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for j in range(self.tag_num):
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s += alpha[k - 1, j] * self.w_exps[j, i]
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alpha[k, i] = x_exps[k, i] * s
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log_likelihood -= x_row_max[k] + np.log(self._l1_norm(alpha[k, :]))
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s = 0.
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for i in range(self.tag_num):
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s += alpha[-1, i] * self.b_exps[i]
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log_likelihood -= np.log(s)
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# calculate the nominator part.
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log_likelihood += (
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self.a[label[0]] + x[0, label[0]] + self.b[label[-1]])
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for k in range(1, seq_len):
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log_likelihood += (x[k, label[k]] + self.w[label[k - 1], label[k]])
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return -log_likelihood
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def crf_forward_compute(self):
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for i in range(self.seq_num):
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start = self.seq_start_positions[i]
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end = self.seq_start_positions[i + 1]
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self.log_likelihood[i] = self._forward_a_sequence(
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self.x[start:end, :], self.x_row_max[start:end, :],
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self.x_exps[start:end, :], self.labels[start:end, :],
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self.alpha[start:end, :])
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return self.alpha, self.log_likelihood
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class TestLinearChainCrfOp(OpTest):
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def set_test_data(self):
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# TODO(caoying) Fix the unittest by: add the boundary cases when
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# sequence lengths are 1, 2, and 3.
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SEQ_NUM = 3
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TAG_NUM = 17
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MAX_SEQ_LEN = 5
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# the linear_chain_crf operator only supports sequence (LoD level = 1)
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lod = [[0]]
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for i in range(SEQ_NUM):
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lod[-1].append(lod[-1][-1] + random.randint(1, MAX_SEQ_LEN))
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emission = np.random.uniform(-1, 1,
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[lod[-1][-1], TAG_NUM]).astype("float64")
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emission_row_max = np.amax(emission, axis=1, keepdims=True)
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emission_exps = np.exp(emission - emission_row_max)
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transition = np.random.uniform(-0.5, 0.5,
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[TAG_NUM + 2, TAG_NUM]).astype("float64")
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transition_exps = np.exp(transition)
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labels = np.random.randint(
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low=0, high=TAG_NUM, size=(lod[-1][-1], 1), dtype="int32")
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self.inputs = {
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"Emission": (emission, lod),
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"Transition": transition,
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"Label": (labels, lod)
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}
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crf = LinearChainCrfForward(lod[0], emission, emission_row_max,
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emission_exps, transition, transition_exps,
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labels)
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alpha, log_likelihood = crf.crf_forward_compute()
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self.outputs = {
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"Alpha": alpha,
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"EmissionExps": emission_exps,
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"TransitionExps": transition_exps,
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"LogLikelihood": log_likelihood
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}
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def setUp(self):
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self.op_type = "linear_chain_crf"
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self.set_test_data()
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["Emission", "Transition"], "LogLikelihood")
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def test_check_grad_ignore_transition(self):
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self.check_grad(
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["Emission"], "LogLikelihood", no_grad_set=set("Transition"))
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if __name__ == "__main__":
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unittest.main()
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