wehub-resource-sync
232 lines
7.4 KiB
C++
232 lines
7.4 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <algorithm>
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#include <string>
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#include <tuple>
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#include <utility>
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#include <vector>
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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/common/place.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "xpu/refactor/math.h"
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namespace phi {
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template <typename T, typename XPUType>
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void XPUElementwise(const XPUContext& dev_ctx,
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const T* x_data,
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const DDim& x_dims,
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const T* y_data,
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const DDim& y_dims,
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int axis,
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T* z_data,
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std::function<int(xpu::Context*,
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const XPUType*,
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const XPUType*,
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XPUType*,
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const std::vector<int64_t>&,
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const std::vector<int64_t>&)> func) {
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int max_dim = std::max(x_dims.size(), y_dims.size());
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axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
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PADDLE_ENFORCE_GE(
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axis,
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0,
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errors::InvalidArgument(
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"Axis should be great than or equal to 0, but received axis is %d.",
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axis));
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PADDLE_ENFORCE_LE(
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axis,
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max_dim,
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errors::InvalidArgument(
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"Axis should be less than or equal to %d, but received axis is %d.",
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max_dim,
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axis));
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std::vector<int64_t> x_dims_vec(max_dim, 1);
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std::vector<int64_t> y_dims_vec(max_dim, 1);
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if (x_dims.size() == max_dim) {
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for (int i = 0; i < max_dim; i++) {
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x_dims_vec[i] = x_dims[i];
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}
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} else {
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for (int i = 0; i < x_dims.size(); i++) {
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x_dims_vec[i + axis] = x_dims[i];
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}
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}
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if (y_dims.size() == max_dim) {
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for (int i = 0; i < max_dim; i++) {
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y_dims_vec[i] = y_dims[i];
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}
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} else {
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for (int i = 0; i < y_dims.size(); i++) {
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y_dims_vec[i + axis] = y_dims[i];
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}
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}
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int ret = 0;
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// For [2, 3] + [] --> [2, 3] + [1, 1]
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// For [] + [2, 3] --> [1, 1] + [2, 3]
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// For [] + [], Use [1] + [1] to replace [], because xpu not support []
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if (x_dims_vec.size() == 0) {
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x_dims_vec = std::vector<int64_t>({1});
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}
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if (y_dims_vec.size() == 0) {
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y_dims_vec = std::vector<int64_t>({1});
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}
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ret = func(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_data),
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reinterpret_cast<const XPUType*>(y_data),
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reinterpret_cast<XPUType*>(z_data),
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x_dims_vec,
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y_dims_vec);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "elementwise");
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}
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template <typename T, typename XPUType>
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void XPUElementwise(const XPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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int axis,
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DenseTensor* z,
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std::function<int(xpu::Context*,
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const XPUType*,
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const XPUType*,
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XPUType*,
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const std::vector<int64_t>&,
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const std::vector<int64_t>&)> func) {
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dev_ctx.template Alloc<T>(z);
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if (z->numel() == 0) {
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return;
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}
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const DDim& x_dims = x.dims();
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const DDim& y_dims = y.dims();
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const T* x_data = x.data<T>();
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const T* y_data = y.data<T>();
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T* z_data = z->data<T>();
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XPUElementwise<T, XPUType>(
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dev_ctx, x_data, x_dims, y_data, y_dims, axis, z_data, func);
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}
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template <typename T, typename XPUType>
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void XPUElementwiseGrad(const XPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& dz,
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int axis,
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DenseTensor* dx,
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DenseTensor* dy,
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std::function<int(xpu::Context*,
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const XPUType*,
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const XPUType*,
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const XPUType*,
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const XPUType*,
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XPUType*,
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XPUType*,
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const std::vector<int64_t>&,
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const std::vector<int64_t>&)> func,
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bool use_x_y_data) {
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auto* z = &dz;
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const DDim& x_dims = x.dims();
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const DDim& y_dims = y.dims();
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int max_dim = std::max(x_dims.size(), y_dims.size());
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axis = (axis == -1 ? std::abs(x_dims.size() - y_dims.size()) : axis);
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PADDLE_ENFORCE_GE(
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axis,
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0,
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errors::InvalidArgument(
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"Axis should be great than or equal to 0, but received axis is %d.",
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axis));
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PADDLE_ENFORCE_LE(
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axis,
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max_dim,
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errors::InvalidArgument(
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"Axis should be less than or equal to %d, but received axis is %d.",
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max_dim,
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axis));
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std::vector<int64_t> x_dims_vec(max_dim, 1);
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std::vector<int64_t> y_dims_vec(max_dim, 1);
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if (x_dims.size() == max_dim) {
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for (int i = 0; i < max_dim; i++) {
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x_dims_vec[i] = x_dims[i];
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}
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} else {
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for (int i = 0; i < x_dims.size(); i++) {
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x_dims_vec[i + axis] = x_dims[i];
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}
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}
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if (y_dims.size() == max_dim) {
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for (int i = 0; i < max_dim; i++) {
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y_dims_vec[i] = y_dims[i];
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}
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} else {
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for (int i = 0; i < y_dims.size(); i++) {
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y_dims_vec[i + axis] = y_dims[i];
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}
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}
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const T* x_data = use_x_y_data ? x.data<T>() : z->data<T>();
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const T* y_data = use_x_y_data ? y.data<T>() : z->data<T>();
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const T* z_data = z->data<T>();
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const T* dz_data = dz.data<T>();
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T* dx_data = nullptr;
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T* dy_data = nullptr;
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if (dx) {
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dx_data = dev_ctx.template Alloc<T>(dx);
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}
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if (dy) {
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dy_data = dev_ctx.template Alloc<T>(dy);
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}
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if (dz.numel() == 0) {
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if (dx) {
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Full<T, XPUContext>(dev_ctx, x.dims(), 0, dx);
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}
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if (dy) {
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Full<T, XPUContext>(dev_ctx, y.dims(), 0, dy);
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}
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return;
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}
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// use [1] to replace [], because xpu not support []
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if (x_dims_vec.size() == 0) {
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x_dims_vec = std::vector<int64_t>({1});
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}
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if (y_dims_vec.size() == 0) {
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y_dims_vec = std::vector<int64_t>({1});
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}
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int ret = func(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_data),
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reinterpret_cast<const XPUType*>(y_data),
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reinterpret_cast<const XPUType*>(z_data),
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reinterpret_cast<const XPUType*>(dz_data),
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reinterpret_cast<XPUType*>(dy_data),
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reinterpret_cast<XPUType*>(dx_data),
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x_dims_vec,
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y_dims_vec);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "elementwise");
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}
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} // namespace phi
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