wehub-resource-sync
126 lines
4.1 KiB
C++
126 lines
4.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/phi/kernels/edit_distance_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/mixed_vector.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T, typename Context>
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void EditDistanceKernel(const Context& dev_ctx,
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const DenseTensor& hyps,
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const DenseTensor& refs,
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const optional<DenseTensor>& hypslength,
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const optional<DenseTensor>& refslength,
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bool normalized,
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DenseTensor* sequencenum,
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DenseTensor* out) {
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int64_t* seq_num_data = dev_ctx.template Alloc<int64_t>(sequencenum);
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auto batch_size = hyps.dims()[0];
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Vector<size_t> hyp_lod(batch_size + 1);
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Vector<size_t> ref_lod(batch_size + 1);
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bool use_length = hypslength.get_ptr() != nullptr;
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if (use_length) {
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// build lod when using padding
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auto hyp_length_ptr = hypslength.get_ptr()->data<int64_t>();
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auto ref_length_ptr = refslength.get_ptr()->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 = hyps.lod()[0];
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ref_lod = refs.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_GT(
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ref_lod[i],
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ref_lod[i - 1],
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errors::InvalidArgument("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->Resize({static_cast<int64_t>(num_strs), 1});
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dev_ctx.template Alloc<T>(out);
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auto outdata = out->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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DenseTensor dist_t;
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dist_t.Resize({m + 1, n + 1});
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dev_ctx.template Alloc<T>(&dist_t);
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auto dist = dist_t.data<T>();
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auto hyp_offset = use_length ? num * hyps.dims()[1] : hyp_lod[num];
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auto ref_offset = use_length ? num * refs.dims()[1] : ref_lod[num];
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auto x1 = hyps.data<int64_t>() + hyp_offset;
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auto x2 = refs.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_GT(
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n,
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0UL,
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errors::InvalidArgument("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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outdata[num] = distance;
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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edit_distance, CPU, ALL_LAYOUT, phi::EditDistanceKernel, float) {
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kernel->OutputAt(0).SetDataType(phi::DataType::INT64);
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}
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