log_softmax_grad_kernel.cc

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle/phi/kernels/log_softmax_grad_kernel.h"

#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/axis_utils.h"

namespace phi {

template <typename T, typename Context>
void LogSoftmaxGradKernel(const Context& dev_ctx,
                          const DenseTensor& out,
                          const DenseTensor& out_grad,
                          int axis,
                          DenseTensor* x_grad) {
  using XPUType = typename XPUTypeTrait<T>::Type;
  const int rank = out.dims().size();
  axis = funcs::CanonicalAxis(axis, rank);

  if (out.numel() != 0) {
    auto out_shape = phi::vectorize<int>(out.dims());
    dev_ctx.template Alloc<T>(x_grad);
    xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
    T* tmp_ptr = RAII_GUARD.alloc_l3_or_gm<T>(out_grad.numel());
    T* tmp2_ptr = RAII_GUARD.alloc_l3_or_gm<T>(out_grad.numel());
    PADDLE_ENFORCE_NE(
        tmp_ptr, nullptr, phi::errors::External("no enough memory in xpu"));
    PADDLE_ENFORCE_NE(
        tmp2_ptr, nullptr, phi::errors::External("no enough memory in xpu"));

    int r = xpu::exp<XPUType>(dev_ctx.x_context(),
                              reinterpret_cast<const XPUType*>(out.data<T>()),
                              reinterpret_cast<XPUType*>(tmp_ptr),
                              out_grad.numel());
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "exp");
    r = xpu::reciprocal<XPUType>(dev_ctx.x_context(),
                                 reinterpret_cast<const XPUType*>(tmp_ptr),
                                 reinterpret_cast<XPUType*>(tmp2_ptr),
                                 out_grad.numel());
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "reciprocal");
    r = xpu::mul<XPUType>(dev_ctx.x_context(),
                          reinterpret_cast<const XPUType*>(tmp2_ptr),
                          reinterpret_cast<const XPUType*>(out_grad.data<T>()),
                          reinterpret_cast<XPUType*>(tmp2_ptr),
                          out_grad.numel());
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "mul");
    r = xpu::softmax_grad<XPUType>(
        dev_ctx.x_context(),
        reinterpret_cast<const XPUType*>(tmp_ptr),
        reinterpret_cast<const XPUType*>(tmp2_ptr),
        reinterpret_cast<XPUType*>(x_grad->data<T>()),
        out_shape,
        axis);
    PADDLE_ENFORCE_XDNN_SUCCESS(r, "softmax_grad");
  }
}

}  // namespace phi

PD_REGISTER_KERNEL(
    log_softmax_grad, XPU, ALL_LAYOUT, phi::LogSoftmaxGradKernel, float) {}