wehub-resource-sync
107 lines
3.5 KiB
C++
107 lines
3.5 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/tril_triu_grad_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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namespace phi {
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template <typename T, typename Context>
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void TrilTriuGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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int diagonal,
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bool lower,
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DenseTensor* x_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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dev_ctx.template Alloc<T>(x_grad);
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auto dy_shape = vectorize<int64_t>(out_grad.dims());
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int r = 0;
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if (lower) {
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r = xpu::tril(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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reinterpret_cast<XPUType*>(x_grad->data<T>()),
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dy_shape,
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static_cast<int64_t>(diagonal));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "tril_op");
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} else {
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r = xpu::triu(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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reinterpret_cast<XPUType*>(x_grad->data<T>()),
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dy_shape,
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static_cast<int64_t>(diagonal));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "triu_op");
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}
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}
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template <typename T, typename Context>
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void TrilGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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int diagonal,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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TrilTriuGradKernel<T, Context>(dev_ctx, out_grad, diagonal, true, x_grad);
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}
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template <typename T, typename Context>
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void TriuGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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int diagonal,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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TrilTriuGradKernel<T, Context>(dev_ctx, out_grad, diagonal, false, x_grad);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(tril_grad,
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XPU,
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ALL_LAYOUT,
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phi::TrilGradKernel,
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int,
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int64_t,
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float,
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phi::float16,
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phi::bfloat16,
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bool) {}
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PD_REGISTER_KERNEL(triu_grad,
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XPU,
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ALL_LAYOUT,
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phi::TriuGradKernel,
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int,
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int64_t,
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float,
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phi::float16,
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phi::bfloat16,
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bool) {}
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PD_REGISTER_KERNEL(tril_triu_grad,
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XPU,
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ALL_LAYOUT,
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phi::TrilTriuGradKernel,
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int,
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int64_t,
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float,
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phi::float16,
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phi::bfloat16,
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bool) {}
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