wehub-resource-sync
101 lines
3.3 KiB
C++
101 lines
3.3 KiB
C++
// Copyright (c) 2021 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 <set>
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#include "paddle/phi/core/visit_type.h"
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#include "paddle/phi/kernels/cast_kernel.h"
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#include "paddle/phi/kernels/funcs/reduce_function.h"
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namespace phi {
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template <typename Context, typename T, typename Functor>
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void Reduce(const Context& dev_ctx,
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const DenseTensor& x,
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bool reduce_all,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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DataType out_dtype,
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DenseTensor* out) {
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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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// If the dims has full dim, set the reduce_all is True
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const int& input_dim_size = x.dims().size();
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std::set<int> dims_set(dims.begin(), dims.end());
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bool full_dim = true;
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for (int i = 0; i < input_dim_size; ++i) {
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if (dims_set.find(i) == dims_set.end() &&
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dims_set.find(i - input_dim_size) == dims_set.end()) {
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full_dim = false;
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break;
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}
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}
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reduce_all = (reduce_all || full_dim);
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// no need to cast dtype
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if (out_dtype == phi::DataType::UNDEFINED || out_dtype == x.dtype()) {
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// do reduce sum
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PD_VISIT_ALL_CPU_TYPES(
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x.dtype(), "ReduceKernelImpl", ([&] {
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funcs::ReduceKernelImpl<Context, T, data_t, Functor>(
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dev_ctx, x, out, dims, keep_dim, reduce_all);
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}));
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} else {
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// cast x tensor to out_dtype
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auto tmp_tensor = Cast<T, Context>(dev_ctx, x, out_dtype);
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// do reduce sum
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PD_VISIT_ALL_CPU_TYPES(
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out_dtype, "ReduceKernelImpl", ([&] {
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funcs::ReduceKernelImpl<Context, T, data_t, Functor>(
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dev_ctx, tmp_tensor, out, dims, keep_dim, reduce_all);
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}));
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}
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}
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template <typename Context, typename T, typename Functor>
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void BoolReduceKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* output) {
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reduce_all = recompute_reduce_all(input, dims, reduce_all);
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dev_ctx.template Alloc<bool>(output);
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// The dims has full dim, set the reduce_all is True
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const auto& input_dim_size = input.dims().size();
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std::set<int> dims_set(dims.begin(), dims.end());
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bool full_dim = true;
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for (auto i = 0; i < input_dim_size; i++) {
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if (dims_set.find(i) == dims_set.end()) {
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full_dim = false;
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break;
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}
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}
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reduce_all = (reduce_all || full_dim);
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DenseTensor tmp_tensor;
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if (input.dtype() != phi::DataType::BOOL) {
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tmp_tensor = Cast<T, Context>(dev_ctx, input, phi::DataType::BOOL);
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} else {
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tmp_tensor = input;
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}
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funcs::ReduceKernelImpl<Context, bool, bool, Functor>(
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dev_ctx, tmp_tensor, output, dims, keep_dim, reduce_all);
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}
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} // namespace phi
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