text: /**
   * Copyright 2021 Huawei Technologies Co., Ltd
   *
   * 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 "ops/ctc_loss_v2_grad.h"
  #include <vector>
  #include <string>
  #include <memory>
  #include <map>
  #include <set>
  #include "ops/op_utils.h"
  #include "utils/check_convert_utils.h"
  #include "abstract/primitive_infer_map.h"
  namespace mindspore {
  namespace ops {
  namespace {
  abstract::ShapePtr CTCLossV2GradInferShape(const PrimitivePtr &primitive,
                                             const std::vector<AbstractBasePtr> &input_args) {
    constexpr size_t kLenLogProbs = 3;
    constexpr int64_t kInputSize = 7;
    constexpr size_t kIdx2 = 2;
    MS_EXCEPTION_IF_NULL(primitive);
    auto prim_name = primitive->name();
    (void)CheckAndConvertUtils::CheckInteger("input numbers", SizeToLong(input_args.size()), kEqual, kInputSize,
                                             prim_name);
    for (const auto &item : input_args) {
      MS_EXCEPTION_IF_NULL(item);
    }
    auto log_probs_shape_map = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[1]->BuildShape());
    auto log_probs_shape = log_probs_shape_map[kShape];
    if (log_probs_shape.size() != kLenLogProbs) {
      MS_LOG(EXCEPTION) << "Input log_probs's dims must be 3, but got :" << log_probs_shape.size();
    }
    int64_t T = log_probs_shape[0];
    int64_t N = log_probs_shape[1];
    int64_t C = log_probs_shape[kIdx2];
    ShapeVector output_shape = {T, N, C};
    return std::make_shared<abstract::Shape>(output_shape);
  }
  
  TypePtr CTCLossV2GradInferType(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
    MS_EXCEPTION_IF_NULL(primitive);
    auto name = primitive->name();
    const std::set<TypePtr> valid_types = {kFloat32};
    std::map<std::string, TypePtr> types;
    MS_EXCEPTION_IF_NULL(input_args[0]);
    MS_EXCEPTION_IF_NULL(input_args[1]);
    (void)types.emplace("grad_out", input_args[0]->BuildType());
    (void)types.emplace("log_probs", input_args[1]->BuildType());
    auto out_type = CheckAndConvertUtils::CheckTensorTypeSame(types, valid_types, name);
    return out_type;
  }
  }  // namespace
  
  AbstractBasePtr CTCLossV2GradInfer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
                                     const std::vector<AbstractBasePtr> &input_args) {
    auto infer_shape = CTCLossV2GradInferShape(primitive, input_args);
    auto infer_type = CTCLossV2GradInferType(primitive, input_args);
    return abstract::MakeAbstract(infer_shape, infer_type);
  }
  REGISTER_PRIMITIVE_EVAL_IMPL(CTCLossV2Grad, prim::kPrimCTCLossV2Grad, CTCLossV2GradInfer, nullptr, true);
  }  // namespace ops
  }  // namespace mindspore
  