wehub-resource-sync
103 lines
3.4 KiB
C++
103 lines
3.4 KiB
C++
// Copyright (c) 2024 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/logsumexp_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/activation_kernel.h"
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#include "paddle/phi/kernels/elementwise_add_kernel.h"
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#include "paddle/phi/kernels/elementwise_subtract_kernel.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/reduce_max_kernel.h"
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#include "paddle/phi/kernels/reduce_sum_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void LogsumexpKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int>& axis,
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bool keepdim,
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bool reduce_all,
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DenseTensor* out) {
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if (x.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), -INFINITY, out);
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return;
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}
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auto xdim = x.dims();
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for (int i = 0; i < xdim.size(); i++)
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PADDLE_ENFORCE_LT(0,
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xdim[i],
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errors::InvalidArgument(
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"The dims of Input(X) should be greater than 0."));
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reduce_all = recompute_reduce_all(x, axis, reduce_all);
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std::vector<int64_t> outdim_vec, keep_outdim_vec;
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std::vector<int> axis_vec;
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int64_t compute_size = 1, other_size = 1;
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for (auto i : axis) {
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auto v = i >= 0 ? i : i + xdim.size();
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axis_vec.push_back(v);
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}
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if (axis.size() == 0 || reduce_all) {
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axis_vec.clear();
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for (int i = 0; i < xdim.size(); i++) {
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axis_vec.push_back(i);
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}
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}
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for (int i = 0; i < xdim.size(); i++) {
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bool flag = false;
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for (auto v : axis_vec) {
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if (v == i) {
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flag = true;
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break;
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}
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}
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if (flag) {
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compute_size *= xdim[i];
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keep_outdim_vec.push_back(1);
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if (keepdim) outdim_vec.push_back(1);
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} else {
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other_size *= xdim[i];
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outdim_vec.push_back(xdim[i]);
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keep_outdim_vec.push_back(xdim[i]);
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}
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}
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auto outdim = make_ddim(outdim_vec);
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auto keep_outdim = make_ddim(keep_outdim_vec);
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// The XPU logsumexp api does not use xmax to normalize its input, so we
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// fallback to the non fusion impl currently.
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DenseTensor max_x;
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max_x.Resize(keep_outdim);
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MaxKernel<T, Context>(dev_ctx, x, axis_vec, true, &max_x);
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DenseTensor temp_x = Subtract<T, Context>(dev_ctx, x, max_x);
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ExpKernel<T, Context>(dev_ctx, temp_x, &temp_x);
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SumKernel<T, Context>(dev_ctx, temp_x, axis_vec, x.dtype(), keepdim, out);
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LogKernel<T, Context>(dev_ctx, *out, out);
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max_x.Resize(outdim);
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out->Resize(outdim);
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AddKernel<T, Context>(dev_ctx, *out, max_x, out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(logsumexp,
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XPU,
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ALL_LAYOUT,
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phi::LogsumexpKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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