wehub-resource-sync
118 lines
4.2 KiB
C++
118 lines
4.2 KiB
C++
// Copyright (c) 2023 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/expand_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/common/scalar.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void ExpandGradKernel(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& shape,
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DenseTensor* in_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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if ((in_grad && in_grad->numel() == 0) || out_grad.numel() == 0) {
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Full<T, Context>(dev_ctx, in_grad->dims(), 0, in_grad);
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return;
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}
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auto in_grad_data = dev_ctx.template Alloc<T>(in_grad);
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auto out_grad_dims = vectorize<int64_t>(out_grad.dims());
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auto in_grad_dims = vectorize<int64_t>(in_grad->dims());
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in_grad_dims.insert(
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in_grad_dims.begin(), out_grad.dims().size() - in_grad->dims().size(), 1);
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// Two zero
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if (out_grad_dims.size() == 0 && in_grad_dims.size() == 0) {
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out_grad_dims = {1};
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in_grad_dims = {1};
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}
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int r = xpu::expand_grad<XPUType>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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reinterpret_cast<XPUType*>(in_grad_data),
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out_grad_dims,
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in_grad_dims);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "expand_grad");
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}
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template <>
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void ExpandGradKernel<double, XPUContext>(const XPUContext& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const IntArray& shape,
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DenseTensor* in_grad) {
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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if ((in_grad && in_grad->numel() == 0) || out_grad.numel() == 0) {
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Full<double, XPUContext>(dev_ctx, in_grad->dims(), 0, in_grad);
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return;
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}
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auto in_grad_data = dev_ctx.template Alloc<double>(in_grad);
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auto out_grad_dims = vectorize<int64_t>(out_grad.dims());
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auto in_grad_dims = vectorize<int64_t>(in_grad->dims());
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in_grad_dims.insert(
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in_grad_dims.begin(), out_grad.dims().size() - in_grad->dims().size(), 1);
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// Two zero
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if (out_grad_dims.size() == 0 && in_grad_dims.size() == 0) {
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out_grad_dims = {1};
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in_grad_dims = {1};
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}
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float* out_grad_fp32 = RAII_GUARD.alloc_l3_or_gm<float>(out_grad.numel());
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float* in_grad_fp32 = RAII_GUARD.alloc_l3_or_gm<float>(in_grad->numel());
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int r = 0;
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r = xpu::cast<double, float>(
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dev_ctx.x_context(),
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reinterpret_cast<const double*>(out_grad.data<double>()),
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out_grad_fp32,
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out_grad.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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r = xpu::expand_grad<float>(dev_ctx.x_context(),
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out_grad_fp32,
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in_grad_fp32,
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out_grad_dims,
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in_grad_dims);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "expand_grad");
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r = xpu::cast<float, double>(dev_ctx.x_context(),
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in_grad_fp32,
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reinterpret_cast<double*>(in_grad_data),
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in_grad->numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(expand_grad,
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XPU,
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ALL_LAYOUT,
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phi::ExpandGradKernel,
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float,
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int64_t,
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double,
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phi::bfloat16,
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phi::float16) {}
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