wehub-resource-sync
263 lines
9.5 KiB
C++
263 lines
9.5 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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#pragma once
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/unique_functor.h"
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namespace phi {
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template <typename InT, typename IndexT, typename Context>
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static void UniqueConsecutiveFlattenedTensor(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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bool return_inverse,
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bool return_counts,
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DenseTensor* inverse,
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DenseTensor* count) {
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const InT* in_data = in.data<InT>();
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std::vector<InT> out_vec(in.numel());
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std::vector<IndexT> inverse_vec(in.numel());
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std::vector<IndexT> counts_vec(in.numel());
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memcpy(out_vec.data(), in_data, in.numel() * sizeof(InT));
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InT* p = out_vec.data();
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int64_t last = 0;
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IndexT* q = counts_vec.data();
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for (int64_t i = 0; i < in.numel(); i++) {
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if (in_data[i] != *p) {
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*(++p) = in_data[i];
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if (return_counts) {
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*(q++) = i - last;
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last = i;
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}
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}
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if (return_inverse) {
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inverse_vec[i] = p - out_vec.data();
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}
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}
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bool is_empty = in.numel() == 0;
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int64_t output_size = is_empty ? 0 : (p - out_vec.data() + 1);
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if (return_counts) {
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if (!is_empty) *q = in.numel() - last;
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counts_vec.resize(output_size);
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}
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out_vec.resize(output_size);
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out->Resize({output_size});
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auto* out_data = dev_ctx.template Alloc<InT>(out);
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std::copy(out_vec.begin(), out_vec.end(), out_data);
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if (return_inverse) {
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inverse->Resize({in.numel()});
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auto* inverse_data = dev_ctx.template Alloc<IndexT>(inverse);
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std::copy(inverse_vec.begin(), inverse_vec.end(), inverse_data);
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}
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if (return_counts) {
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count->Resize({out->numel()});
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auto* counts_data = dev_ctx.template Alloc<IndexT>(count);
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std::copy(counts_vec.begin(), counts_vec.end(), counts_data);
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}
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}
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template <typename Context, typename InT>
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struct UniqueConsecutiveFlattenedTensorFunctor {
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const Context& dev_ctx_;
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const DenseTensor& in_;
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DenseTensor* out_;
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const bool return_inverse_;
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const bool return_counts_;
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DenseTensor* inverse_;
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DenseTensor* count_;
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UniqueConsecutiveFlattenedTensorFunctor(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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bool return_inverse,
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bool return_counts,
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DenseTensor* inverse,
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DenseTensor* count)
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: dev_ctx_(dev_ctx),
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in_(in),
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out_(out),
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return_inverse_(return_inverse),
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return_counts_(return_counts),
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inverse_(inverse),
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count_(count) {}
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template <typename IndexT>
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void apply() const {
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UniqueConsecutiveFlattenedTensor<InT, IndexT, Context>(
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dev_ctx_, in_, out_, return_inverse_, return_counts_, inverse_, count_);
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}
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};
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template <typename Context, class ForwardIt, typename InT, typename IndexT>
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static ForwardIt UniqueConsecutiveDimImpl(
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const Context& dev_ctx UNUSED,
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ForwardIt first,
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ForwardIt last,
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const std::vector<IndexT>& sorted_indices_vec,
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std::vector<IndexT>* inverse_vec,
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std::vector<IndexT>* counts_vec) {
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if (first == last) {
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return last;
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}
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(*inverse_vec)[sorted_indices_vec[0]] = 0;
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(*counts_vec)[0] = 1;
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ForwardIt begin = first;
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ForwardIt result = first;
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while (++first != last) {
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int64_t idx_first = std::distance(begin, first);
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int64_t idx_result = std::distance(begin, result);
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if (!funcs::Equal<InT>(*result, *first)) {
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if (++result != first) {
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*result = std::move(*first);
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}
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idx_result += 1;
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}
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(*inverse_vec)[sorted_indices_vec[idx_first]] = idx_result;
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(*counts_vec)[idx_result] += 1;
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}
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return ++result;
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}
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template <typename Context, typename InT, typename IndexT>
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static void UniqueConsecutiveDim(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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bool return_inverse,
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bool return_counts,
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int axis,
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DenseTensor* inverse,
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DenseTensor* count) {
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// transpose tensor: eg. axis=1, [dim0, dim1, dim2] -> [dim1, dim0, dim2]
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std::vector<int> permute(in.dims().size());
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std::iota(permute.begin(), permute.end(), 0);
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permute[axis] = 0;
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permute[0] = axis;
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std::vector<int64_t> in_trans_dims_vec(vectorize(in.dims()));
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in_trans_dims_vec[axis] = in.dims()[0];
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in_trans_dims_vec[0] = in.dims()[axis];
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DenseTensor in_trans;
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DDim in_trans_dims = make_ddim(in_trans_dims_vec);
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in_trans.Resize(in_trans_dims);
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dev_ctx.template Alloc<InT>(&in_trans);
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funcs::TransCompute<Context, InT>(
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in.dims().size(), dev_ctx, in, &in_trans, permute);
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// reshape tensor: eg. [dim1, dim0, dim2] -> [dim1, dim0*dim2]
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DDim in_trans_flat_dims = common::flatten_to_2d(in_trans_dims, 1);
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in_trans.Resize(in_trans_flat_dims);
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std::vector<IndexT> sorted_indices_vec(in_trans.dims()[0]);
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std::iota(sorted_indices_vec.begin(), sorted_indices_vec.end(), 0);
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int64_t col = in_trans.dims()[1];
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const InT* in_trans_data = in_trans.data<InT>();
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// sort tensor according to indices
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DenseTensor input_sorted;
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input_sorted.Resize(in_trans_dims);
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dev_ctx.template Alloc<InT>(&input_sorted);
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InT* input_sorted_data = input_sorted.data<InT>();
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for (size_t i = 0; i < sorted_indices_vec.size(); ++i) {
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memcpy(input_sorted_data + i * col,
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in_trans_data + static_cast<int64_t>(sorted_indices_vec[i]) * col,
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col * sizeof(InT));
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}
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std::vector<DenseTensor> input_unbind = funcs::Unbind(input_sorted);
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std::vector<IndexT> inverse_vec(sorted_indices_vec.size(), 0);
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std::vector<IndexT> counts_vec(sorted_indices_vec.size(), 0);
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auto last = UniqueConsecutiveDimImpl<Context,
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std::vector<DenseTensor>::iterator,
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InT>(dev_ctx,
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input_unbind.begin(),
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input_unbind.end(),
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sorted_indices_vec,
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&inverse_vec,
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&counts_vec);
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input_unbind.erase(last, input_unbind.end());
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counts_vec.erase(counts_vec.begin() + input_unbind.size(), counts_vec.end());
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funcs::ConcatFunctor<Context, InT> concat_functor;
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DenseTensor out_trans;
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std::vector<int64_t> out_trans_dims_vec = in_trans_dims_vec;
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out_trans_dims_vec[0] = input_unbind.size();
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out_trans.Resize(out_trans_dims_vec);
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dev_ctx.template Alloc<InT>(&out_trans);
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std::swap(out_trans_dims_vec[0], out_trans_dims_vec[axis]);
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out->Resize(out_trans_dims_vec);
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dev_ctx.template Alloc<InT>(out);
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concat_functor(dev_ctx, input_unbind, 0, &out_trans);
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funcs::TransCompute<Context, InT>(
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out_trans.dims().size(), dev_ctx, out_trans, out, permute);
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if (return_inverse) {
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TensorFromVector(inverse_vec, dev_ctx, inverse);
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}
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if (return_counts) {
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TensorFromVector(counts_vec, dev_ctx, count);
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}
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}
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template <typename Context, typename InT>
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struct UniqueConsecutiveDimFunctor {
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const Context& dev_ctx_;
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const DenseTensor& in_;
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DenseTensor* out_;
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const int axis_;
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const bool return_inverse_;
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const bool return_counts_;
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DenseTensor* inverse_;
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DenseTensor* count_;
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UniqueConsecutiveDimFunctor(const Context& dev_ctx,
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const DenseTensor& in,
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DenseTensor* out,
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const int axis,
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bool return_inverse,
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bool return_counts,
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DenseTensor* inverse,
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DenseTensor* count)
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: dev_ctx_(dev_ctx),
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in_(in),
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out_(out),
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axis_(axis),
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return_inverse_(return_inverse),
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return_counts_(return_counts),
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inverse_(inverse),
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count_(count) {}
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template <typename IndexT>
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void apply() const {
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UniqueConsecutiveDim<Context, InT, IndexT>(dev_ctx_,
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in_,
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out_,
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return_inverse_,
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return_counts_,
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axis_,
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inverse_,
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count_);
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}
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};
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} // namespace phi
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