text: // 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.
  
  #pragma once
  
  #include <vector>
  
  #include "paddle/fluid/operators/math/sequence_padding.h"
  #include "paddle/fluid/operators/math/sequence_scale.h"
  #include "paddle/phi/backends/dynload/warpctc.h"
  #include "paddle/phi/core/dense_tensor.h"
  #include "paddle/phi/kernels/empty_kernel.h"
  #include "paddle/phi/kernels/funcs/eigen/common.h"
  #include "paddle/phi/kernels/funcs/math_function.h"
  #include "paddle/utils/optional.h"
  
  namespace phi {
  
  template <typename T, typename Context>
  void WarpctcGradKernel(const Context& dev_ctx,
                         const DenseTensor& warpctc_grad,
                         const DenseTensor& logits,
                         const DenseTensor& loss_grad,
                         const paddle::optional<const DenseTensor&> logits_length,
                         int blank,
                         bool norm_by_times,
                         DenseTensor* logits_grad) {
    dev_ctx.template Alloc<T>(logits_grad);
  
    if (logits_length.is_initialized()) {
      int max_seq_length = warpctc_grad.dims()[0];  // Tmax
      int num_sequences = warpctc_grad.dims()[1];   // B
      int seq_width = warpctc_grad.dims()[2];       // D
  
      // B
      auto logits_len_e = EigenTensor<int64_t, 1>::From(*logits_length);
      // (B, 1)
      auto loss_grad_e = EigenTensor<T, 2>::From(loss_grad);
      // (T, B, D)
      auto warpctc_grad_e = EigenTensor<T, 3>::From(warpctc_grad);
  
      auto logits_grad_e = EigenTensor<T, 3>::From(*logits_grad);
  
      Eigen::DSizes<int, 3> grad_shape(1, num_sequences, 1);
      Eigen::DSizes<int, 3> bcast(max_seq_length, 1, seq_width);
      auto logits_g = warpctc_grad_e *
                      loss_grad_e.reshape(grad_shape).broadcast(bcast).eval();
  
      auto* place = dev_ctx.eigen_device();
      if (norm_by_times) {
        auto scales = logits_len_e.cast<T>()
                          .inverse()
                          .reshape(grad_shape)
                          .broadcast(bcast)
                          .eval();
        logits_grad_e.device(*place) = logits_g * scales;
      } else {
        logits_grad_e.device(*place) = logits_g;
      }
    } else {
      paddle::operators::math::UnpaddingLoDTensorFunctor<Context, T>()(
          dev_ctx,
          warpctc_grad,
          logits_grad,
          -1,
          0,
          norm_by_times,
          paddle::operators::math::kLengthBatchWidth);
  
      const T* loss_grad_data = loss_grad.data<T>();
      paddle::operators::math::ScaleLoDTensorFunctor<Context, T>()(
          dev_ctx, loss_grad_data, logits_grad);
    }
  }
  
  }  // namespace phi
  