wehub-resource-sync
110 lines
4.1 KiB
C++
110 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/pad3d_grad_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/backends/xpu/xpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void Pad3dGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const IntArray& paddings,
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const std::string& mode,
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double pad_value,
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const std::string& data_format,
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DenseTensor* x_grad) {
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T value = static_cast<T>(pad_value);
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std::vector<int64_t> pads = paddings.GetData();
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auto* d_out = &out_grad;
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auto* d_in = x_grad;
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auto d_in_dims = vectorize<int64_t>(d_in->dims());
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const T* d_out_data = d_out->data<T>();
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T* d_in_data = dev_ctx.template Alloc<T>(d_in);
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if (x.numel() == 0) return;
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bool is_ncdhw = true;
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if (data_format == "NDHWC") {
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is_ncdhw = false;
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}
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const int64_t num = d_in_dims[0]; // n
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int64_t channels = d_in_dims[1]; // c
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int64_t in_depth = d_in_dims[2]; // xd
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int64_t in_height = d_in_dims[3]; // xh
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int64_t in_width = d_in_dims[4]; // xw
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if (data_format == "NDHWC") {
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channels = d_in_dims[4];
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in_depth = d_in_dims[1];
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in_height = d_in_dims[2];
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in_width = d_in_dims[3];
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}
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std::vector<int64_t> pads_xpu(6);
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pads_xpu[0] = pads[4]; // pf
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pads_xpu[1] = pads[5]; // pb
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pads_xpu[2] = pads[2]; // pt
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pads_xpu[3] = pads[3]; // pd
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pads_xpu[4] = pads[0]; // pl
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pads_xpu[5] = pads[1]; // pr
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if (mode == "reflect") {
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int r = xpu::reflection_pad3d_grad(dev_ctx.x_context(),
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d_out_data,
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d_in_data,
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num,
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channels,
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in_depth,
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in_height,
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in_width,
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pads_xpu,
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is_ncdhw);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "reflection_pad3d_grad");
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} else if (mode == "replicate") {
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int r = xpu::replication_pad3d_grad(dev_ctx.x_context(),
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d_out_data,
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d_in_data,
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num,
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channels,
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in_depth,
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in_height,
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in_width,
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pads_xpu,
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is_ncdhw);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "replication_pad3d_grad");
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} else if (mode == "constant") {
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int r = xpu::constant_pad3d_grad(dev_ctx.x_context(),
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d_out_data,
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d_in_data,
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num,
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channels,
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in_depth,
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in_height,
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in_width,
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pads_xpu,
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value,
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is_ncdhw);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant_pad3d_grad");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(pad3d_grad, XPU, ALL_LAYOUT, phi::Pad3dGradKernel, float) {}
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