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122 lines
4.1 KiB
122 lines
4.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include <algorithm>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/mixed_vector.h"
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class EditDistanceKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* out_t = ctx.Output<framework::Tensor>("Out");
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auto* x1_t = ctx.Input<framework::LoDTensor>("Hyps");
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auto* x2_t = ctx.Input<framework::LoDTensor>("Refs");
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auto* sequence_num = ctx.Output<framework::Tensor>("SequenceNum");
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int64_t* seq_num_data = sequence_num->mutable_data<int64_t>(ctx.GetPlace());
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auto batch_size = x1_t->dims()[0];
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auto normalized = ctx.Attr<bool>("normalized");
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framework::Vector<size_t> hyp_lod(batch_size + 1);
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framework::Vector<size_t> ref_lod(batch_size + 1);
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bool use_length = ctx.HasInput("HypsLength");
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if (use_length) {
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// build lod when using padding
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auto hyp_length_ptr =
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ctx.Input<framework::Tensor>("HypsLength")->data<int64_t>();
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auto ref_length_ptr =
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ctx.Input<framework::Tensor>("RefsLength")->data<int64_t>();
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for (auto i = 0; i < batch_size; i++) {
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hyp_lod[i + 1] = hyp_lod[i] + hyp_length_ptr[i];
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ref_lod[i + 1] = ref_lod[i] + ref_length_ptr[i];
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}
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} else {
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hyp_lod = x1_t->lod()[0];
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ref_lod = x2_t->lod()[0];
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}
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if (normalized) {
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for (size_t i = 1; i < ref_lod.size(); ++i) {
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PADDLE_ENFORCE(ref_lod[i] > ref_lod[i - 1],
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"Reference string %d is empty.", i);
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}
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}
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auto num_strs = hyp_lod.size() - 1;
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*seq_num_data = static_cast<int64_t>(num_strs);
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out_t->Resize({static_cast<int64_t>(num_strs), 1});
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out_t->mutable_data<float>(ctx.GetPlace());
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auto out = out_t->data<T>();
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T distance = 0.0;
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for (size_t num = 0; num < num_strs; ++num) {
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auto m = static_cast<int64_t>(hyp_lod[num + 1] - hyp_lod[num]);
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auto n = static_cast<int64_t>(ref_lod[num + 1] - ref_lod[num]);
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if (m == 0) {
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distance = n;
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} else if (n == 0) {
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distance = m;
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} else {
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framework::Tensor dist_t;
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dist_t.Resize({m + 1, n + 1});
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dist_t.mutable_data<T>(ctx.GetPlace());
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auto dist = dist_t.data<T>();
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auto hyp_offset = use_length ? num * x1_t->dims()[1] : hyp_lod[num];
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auto ref_offset = use_length ? num * x2_t->dims()[1] : ref_lod[num];
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auto x1 = x1_t->data<int64_t>() + hyp_offset;
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auto x2 = x2_t->data<int64_t>() + ref_offset;
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for (int64_t i = 0; i < m + 1; ++i) {
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dist[i * (n + 1)] = i;
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}
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for (int64_t j = 0; j < n + 1; ++j) {
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dist[j] = j;
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}
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for (int64_t i = 1; i < m + 1; ++i) {
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for (int64_t j = 1; j < n + 1; ++j) {
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int cost = x1[i - 1] == x2[j - 1] ? 0 : 1;
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int dels = dist[(i - 1) * (n + 1) + j] + 1;
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int ins = dist[i * (n + 1) + (j - 1)] + 1;
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int subs = dist[(i - 1) * (n + 1) + (j - 1)] + cost;
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dist[i * (n + 1) + j] = std::min(dels, std::min(ins, subs));
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}
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}
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distance = dist[m * (n + 1) + n];
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}
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if (normalized) {
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PADDLE_ENFORCE(n > 0,
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"The reference string (#%d) cannot be empty "
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"when Attr(normalized) is enabled.",
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n);
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distance = distance / n;
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}
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out[num] = distance;
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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