embedding_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/embedding_grad_kernel.h"

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

namespace phi {

template <typename T, typename Context>
void EmbeddingGradKernel(const Context& ctx,
                         const DenseTensor& input,
                         const DenseTensor& weight,
                         const DenseTensor& out_grad,
                         int64_t padding_idx,
                         DenseTensor* weight_grad) {
  DDim table_dim;
  table_dim = weight.dims();

  auto ids_t = &input;
  auto d_output_t = &out_grad;
  auto d_table_t = weight_grad;

  int64_t ids_numel = ids_t->numel();
  PADDLE_ENFORCE_EQ(
      ids_numel <= std::numeric_limits<int32_t>::max(),
      true,
      phi::errors::OutOfRange(
          "Number of ids greater than int32_t::max , please check "
          "number of ids in LookupTableV2GradXPUKernel."));

  auto& dev_ctx = ctx;
  const int64_t* ids_data = ids_t->data<int64_t>();
  const T* d_output_data = d_output_t->data<T>();
  T* d_table_data = dev_ctx.template Alloc<T>(d_table_t);
  int xm = d_table_t->dims()[0];
  int ym = static_cast<int>(ids_numel);
  int n = d_table_t->dims()[1];

  int r = xpu::embedding_grad<T, int64_t>(dev_ctx.x_context(),
                                          d_output_data,
                                          ids_data,
                                          d_table_data,
                                          xm,
                                          n,
                                          ym,
                                          padding_idx);
  PADDLE_ENFORCE_XDNN_SUCCESS(r, "embedding_grad");
}

}  // namespace phi

PD_REGISTER_KERNEL(
    embedding_grad, XPU, ALL_LAYOUT, phi::EmbeddingGradKernel, float) {}